In this article, I will delve into whether artificial intelligence (AI), particularly the emergence of tools like generative AI (GenAI), will replace classical software outsourcing as we know it.

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In our rapidly evolving work-from-anywhere world, employee downtime and productivity are rising concerns. Just ask Chetu— (sidenote: wrongful termination laws were not on their side). the Florida-based company that came under fire for terminating an employee who turned off their webcam While I’m not on board with you watching your employees via webcam all day, the productivity concerns are real and valid. The important thing to note is that most employees to be productive. They aren’t shirking responsibilities behind a turned-off webcam; they are trying to get their work done as efficiently as possible—and they’re being slowed down by poor DEX. want What Is DEX? DEX, or the digital employee experience, is the way employees interact with technology during their workday. And it’s a key factor in evaluating how an organization stacks up to the rest in the race for effective tech. DEX measures the quality of your workforce’s interaction with the tools they use daily. These tools help them get their jobs done and your company running. So, why are some companies still behind in adapting to new technologies that can help their employees stay productive? Many companies are stuck using tech tools that don’t serve the modern workforce. These inefficient tools slow down operations and ultimately make your employees feel unproductive. In a survey facilitated by Lakeside Software, respondents indicated that they lose about one hour of weekly work time to lousy tech. Naturally, this leaves them frustrated and feeling less than satisfied with the tools their company provides them, which could ultimately lead to dissatisfaction with the company overall. Here are a few tips on how companies can better support their employees. Evaluate Your DEX Maturity. The first step is to look honestly at your company’s DEX efforts and see how you can improve. helps you evaluate where you’re lacking and be honest about what changes need to be made to benefit your employees and, ultimately, your bottom line. Exploring your DEX maturity After all, the two are intrinsically linked—companies with poor turnover rates burn through a lot of cash trying to find new employees, only to have them leave again a year later. The proof is in the pudding: 14% of the employees surveyed said they’d previously left an organization due to poor DEX, and over 60% said they would consider it. When looking into DEX, start at the beginning: clearly evaluate where your company is at to see how you can best improve. A true DEX leader is fully committed to improving the digital employee experience and seeks to make it part of the business's core strategy. They’re willing to invest in IT resources as part of their proactive approach and closely track the impact of their efforts across functions—whether that’s human resources, finance, operations, or another area. They know that optimized digital experiences drive better customer experiences and invest in them accordingly. Quell employee frustrations through comprehensive DEX efforts. Investing in your employees should seem like an obvious step. Still, many employees aren’t just frustrated with the loss of productivity—they’re also frustrated with the overall IT infrastructure that supports their workplace. When investing in DEX, enterprises need to consider how they can and begin to take proactive steps toward improving their tech stack. remove silos between departments Comprehensive DEX efforts take on the following qualities: They’re proactive instead of reactive. They don’t just respond to emergencies as they come up. Instead, they create systems to prevent these issues from ever happening in the first place. They break down silos and become more integrated. They do away with disparate teams and workflows and see the company from a holistic point of view. They go from manual to automated. They adapt to automated systems that can remediate, update, patch, and more. They go from being IT-dependent to self-reliant. This doesn’t mean you don’t need IT—it means you have an “always on” IT infrastructure that supports users with self-help remediations instead of limiting their support to when IT is available. They use complete data instead of incomplete information. They can gather objective system details to make better decisions with accurate metrics. They go from static to predictive analysis. They leverage the power of AI and machine learning to study patterns and trends that could negatively impact their digital environments. They go from one-size-fits-all to personalized IT. This one is really important to pay attention to and is perhaps the biggest mindset shift for companies. Personalized IT provides end users with exactly what they need, like specific hardware and software that suit their unique job responsibilities and work styles. For some companies, the above list will seem like a major change. But for companies looking to become DEX leaders (rather than webcam spies), this list will be essential moving forward. DEX can reduce employee turnover and frustration, increase your output, and heighten your productivity—this is one investment that’s really a no-brainer. Photo by on Annie Spratt Unsplash

How Effective Tech Can Reduce Employee Downtime and Attrition

Collaboration between healthcare and technology industries is moving faster than light. The Healthcare Information and Management Systems Society (HIMSS) in their 2021 Future of Healthcare research that most healthcare systems are planning to increase their investments in digital healthcare tools. reported There is a myriad of reasons for that. The increase in the demand for personalized medicine and the pressure to cut down on healthcare costs has further impacted the need to improve healthcare and technology collaborations further. And that’s not all of it. Following the pandemic, there has been an increase in the need to digitize healthcare further. This has prompted many healthcare providers to lean on the technology that provides digitized delivery of healthcare services in the future. Now, we will look at the top technology trends that your healthcare facility can latch onto in 2022. So, put on your seat belts! 7 Emerging Technology Trends in Healthcare in 2022 Here is a list of top healthcare technology trends expected to transform the healthcare industry in 2022: Internet of Medical Things (IoMT) Cognitive automation (CA) Telemedicine Virtual Reality (VR) AI-empowered Big Data and data analytics Digital Therapeutics Robotic Process Automation (RPA) 1. Internet of Medical Things (IoMT) - think IoT but medically There is the Internet of Things (IoT). So, we shouldn’t be surprised if something else pops up. The Internet of Medical Things is a connected infrastructure of healthcare systems and services that provides adequate care to rural and isolated communities. This technology also aims to provide adequate care to patients who cannot physically meet up with their healthcare provider. IoMT is propelled by innovation and data integration that enables patients to receive timely and high-quality treatment in the comfort of their homes. Here’s how it works: it involves connected digital devices that help to collect, store and convey data through a digital network without human assistance. These connected digital devices include wearables and trackers, which are a massive part of the development of IoMT. For instance, trackers allow people to access real-time data on their behavior daily and proffer recommendations. IoMTs software encourages patients to increase their motivation to lead a healthy lifestyle with features that allow them to track their progress daily or weekly. Through this technology, your business can easily see your patients in a more active capacity than ever. Plus, it’s cost-effective! The data set from this emerging technology includes the patient’s sleep patterns, nutrition habits, average activity level, and other parameters. Healthcare providers can build a more realistic tracking program with these essential parameters to accurately track patients’ progress. With IoMT facilitating telehealth, here are a few ways different healthcare businesses or facilities can take advantage: assisted living technologies; disability and rehabilitation assistive tech; speech assistive tech; chronic disease monitoring technologies. 2. Cognitive Automation - who says your healthcare facility can have its own Alexa? Cognitive automation is a new and fast-emerging technology in digital transformation. It is listed as one of the emerging IT trends in healthcare, and its popularity is expected to increase by 2022. The Cognitive Automation (CA) technology seeks to mimic the behavior of humans beyond repetitive tasks. It serves as the digital brain of the healthcare organization. CA takes the automation capabilities of current software providers and applies Machine Learning algorithms to introduce decision velocity in the healthcare industry. This technology makes it possible to process zettabytes of data within seconds, thereby providing decision-makers with recommendations readily available and backed with real-time data. The end goal of this emerging healthcare technology is to establish a self-driving enterprise whereby operational processes are automated. What say you try out cognitive automation programs to automate your operational processes, cut costs, and maximize your business efforts? 3. Telemedicine - reach more without expanding The pandemic has provided the healthcare industry with the option of turning telemedicine into a current trend for the industry. COVID-19 contributed to the increase in the provision of virtual healthcare, which includes scheduling online appointments, provision of remote care, and video conferencing. Telemedicine is a healthcare technology practice that unlocks the hands-free communication channel. Healthcare providers and patients can get across in real-time virtually. The pandemic opened the potential in this emerging technology as monitoring cases of patients in isolation can be done remotely. In general, it helps to contribute to health equity. This means that healthcare is provided for people of all groups, no matter their geographical, economic, political, and social standing conditions. The emergence of this new technology is a giant leap in how healthcare is provided and is expected to be popularized in the coming years. No physical appointments; no schedules, except a patient’s condition calls for it. For your healthcare facility, this means wider reach without having to allocate half of your budget to that expansion. 4. Virtual Reality (VR) - give your doctors more tools to work with There has been a recent use in augmented reality and virtual reality, but these areas will need applications in the coming years. This technology serves as a supplement for many activities undertaken by healthcare providers, whether done on-site or on a remote basis. Virtual reality technology has been used in healthcare to: provide surgical training; visualize real-case scenarios to clinical students; make clinic tours; advance the quality of analysis provided to lung scans and treat critical health conditions. Virtual Reality technology helps people with developmental disorders such as ASD to train their social and coping skills in virtual environments. Thanks to virtual reality in healthcare, doctors can continuously train and improve their skills by using virtual environments. It significantly decreases the probability of medical errors and improves value-based care. This is a plus for your healthcare facility as it helps to ensure all your medical providers have all the resources to meet the needs of a patient. 5. AI-empowered Big Data and data analytics - process data faster The introduction of AI technology in the healthcare industry and data analytics will save the industry a significant ton of money in the coming years. Let’s put it this way: it will save YOUR healthcare facility a ton of money. The long-distance analysis and measurement of patient data are healthcare technology trends in the coming years. A large volume of information is created every second, culminating in extensive data needed to be processed. Processing this data manually could lead to the loss of a massive amount of data, which is why AI-powered data processing exists to help prevent this. Concerning healthcare, Big Data can derive from the following: electronic health data (e.g., EMR, ENR, images demographics); biometric data from portable wellness trackers; biobanks to aid with research-design efforts. The need for the automated process of Big Data instead of humans is inevitable as it provides real-time analytics for decision making. AI-empowered Data analytics also helps make predictions in which accurate predictive analysis is completed. This helps to gain insights on previous patient interactions, thereby making predictive analysis needed for changes to occur. This technology will help patients get accurate treatment plans and timely services. It also, in turn, helps healthcare providers enjoy the optimization of their activity and a greater level of control over the supply chain. 6. Digital Therapeutics - take evidence-based care to level 100! Digital Therapeutics is a patient-centered trend in healthcare that delivers evidence-based care via software. It represents a digital health sector that comprises technologies, products, and services spanning the healthcare industry. Digital Therapeutics is an evidence-based behavioral treatment delivered digitally and facilitated by software that helps enhance the accessibility and effectiveness of healthcare. It also helps to prevent, manage and treat diseases. Digital Therapeutics is also used with drugs and medical devices that help improve the care provided to patients. The digital therapeutics technology helps monitor blood sugar readings, offer medication regimens, diet, and offer clinicians a complete picture of the status of the patient’s health. These services provided by this technology helps to provide a compelling survey of patients’ lifestyles and daily activities. This helps to manage some critical health conditions in the patient offering personalized therapies for anxiety and depression. An increasing number of healthcare companies are taking a thoughtful approach to Digital Therapeutics, and it is expected to be an emerging technology in the healthcare sector by 2022. 7. Robotics Process Automation (RPA) Robotic process automation empowered technology has become a significant milestone in healthcare information technology development. This emerging technology makes it possible for health providers to: experience accurate automation; cost reduction; staff optimization; and the introduction of transformational changes. The inclusion of AI algorithms in this technology is helping to provide a guide to patients in need of healthcare providers through the scanning of their symptoms. Thus offering cutting-edge diagnosis techniques for them more accurately than the conventional search engines. The Robotics Process Automation has introduced some robots that can mimic the behavior of humans to the healthcare sector. This robot in development helps to offer numerous capabilities, including the detection of screenwriting, the input of data, and the implementation of pre-defined actions. The RPA technology enables quick scheduling and accurate appointment setting in the hospital. It helps to detect the relevant personal information about the patient quickly, pulls rapid diagnostics of the patient’s symptoms, and pulls the patient’s insurance details. Besides, RPA helps to schedule appointments with a doctor as it checks for free spots in the schedules of the doctors working in all hospitals close to the patient’s location. In recent times, healthcare organizations have widely applied the Robotics Process Automation to: collect and handle medical information more efficiently, enhance appointment scheduling, deliver optimal care to their patients, and improve their hospital organization management processes. Moreover, this is predicted to further develop in the coming years, making it an emerging healthcare technology area in 2022. Now, keep in mind that executing any or all of these trends is as crucial as gaining knowledge. You want to work with companies in the technology sector that can expertly develop these types of software and effectively integrate them into your system for optimal functionality. We have identified the top 3 custom software development companies for healthcare providers. We selected these companies based on testimonials, healthcare-related case studies, regulatory-bound procedures, customer satisfaction, and awards. Here they are: Top 3 Healthcare Software Development Providers 1. Altoros Labs Altoros Labs is one of the largest healthcare software development companies out there. Their wide range of healthcare solutions is designed to improve your business functions and minimize costs. They cater to clients’ unique needs from the healthcare background and render unsurpassable and result-oriented healthcare software development services. Altoros Labs is situated in 10 different countries globally to provide scalable healthcare solutions readily for their clients. With Altoros Labs, you get GDPR- and HIPAA-compliant healthcare technology solutions. Their services include: creation of patient portals; designing solutions for clinical management; development of hospital management systems; development of remote patient monitoring (RPM) systems; custom pharmacy management solutions; revenue cycle management software building. to this healthcare software development provider to get started with discovering the scalable software that is right for your business and take advantage of all the opportunities that 2022 brings. Reach out 2. Chetu Chetu is a healthcare software development company that has earned a reputation of being one of the best and preferred healthcare software development companies globally. They help to provide the right strategy and the right technology needed to develop software for healthcare organizations, enabling them to move in the forward direction. The company has gotten diverse experience in assisting healthcare organizations upscale their business delivering back-to-back successful projects for their clients. Their software products are enriched with sophisticated modern-day technology, which helps to provide their user with an unparalleled user experience. Reach out to them to develop healthcare technology apt to boost your business operations. here 3. Intellectsoft Intellectsoft has carved a unique position among the most reliable healthcare software development agencies. Its cutting-edge transformative solutions and customer-centric approach make it one of the most sought-after software development firms for healthcare providers. The company provides its clients with: healthcare web/mobile applications; digital healthcare solutions; IoT-powered medical solutions; EHR systems; clinical data management. Reach out to them to get started with your healthcare facility to keep up with digital transformation in 2022. here The Bottom Line And there you have it, the top healthcare technology trends in 2022 that your business can latch onto quickly towards efficacy. The technology industry is infiltrating every business sector, and the healthcare industry is no different. This means that to move ahead, stay ahead and be relevant; your health center needs to make do with this trend. And what's more, the technology infiltration is a welcomed one for your business as it can single-handedly transform your operations for the better. So, what do you say?

7 Healthcare Technology Trends to Watch Out for in 2022

Technology influences all aspects of human life, and healthcare is no exception. The rapid technological evolution makes hospitals and other healthcare providers look for solutions that simplify and accelerate their routine. The market for healthcare software development , accounting for USD 13,100 million in 2017 and being forecasted to reach USD 19,300 million by the end of 2025. More and more companies join the competition every year, so it has become difficult to find a reliable provider with proper skills, competencies, certifications, and domain knowledge. is already huge Below, you will find a list of medical software development companies that have proved to be experienced partners when creating robust healthcare software for providers in the US and worldwide. This compilation was based on a thorough assessment of the market leaders based on the following criteria: major healthcare clients, market presence, experience in the healthcare industry, and clients’ reviews. 1. Itransition Colorado-based Itransition is a custom software development company that delivers software solutions for medical organizations and care providers. It creates patient portals, solutions for clinical management, mobile health applications, and more. Employing virtual & augmented reality, IoT, and artificial intelligence, Itransition provides solutions for advanced diagnostics. Itransition’s healthcare software is compliant with HIPAA and other healthcare-related policies. The company’s team of more than 1500 IT specialists delivers healthcare software development to more than 30 countries. <a href="https://medium.com/media/f45777b0568fe3f2a0a9eef2d446b79d/href">https://medium.com/media/f45777b0568fe3f2a0a9eef2d446b79d/href</a> Denver, CO Location: 1998 Founded: $10,000+ Min. Project Budget: Summer Infant, Maximus Major Healthcare Customers: 1000–9999 Average Number of Employees: Website: https://www.itransition.com/ 2. Chetu Since 2000, Chetu has been a medical software development company. The company’s service list includes the development of HIPAA-compliant electronic health records (EHR) and health information exchange (HIE) solutions, electronic prescription services, patient management systems, and many more. The company also delivers solutions for individuals with disabilities. <a href="https://medium.com/media/9c19c7a2e8e80d5e1d5e4d336d1cf281/href">https://medium.com/media/9c19c7a2e8e80d5e1d5e4d336d1cf281/href</a> Plantation, FL Location: 2000 Founded: $10,000+ Min. Project Budget: Memorial Healthcare System, Conifer Health Solutions, MedAdept Major Healthcare Customers: 1000–9999 Average Number of Employees: Website: https://www.chetu.com/ 3. Macadamian Founded in 1997, Macadamian is a full-service software development firm with a focus on healthcare solutions delivering. The company develops of a wide range of healthcare solutions, including cloud-based care management products, digital healthcare solutions for patients, and many more. All of the products are made in compliance with HIPAA and FDA regulations. <a href="https://medium.com/media/47a5910134419ca2c8b5801044afb196/href">https://medium.com/media/47a5910134419ca2c8b5801044afb196/href</a> Gatineau, Canada Location: 1997 Founded: $50,000+ Min. Project Budget: Siemens Healthineers, Bio-Optronics, Fresenius Medical Care Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://www.macadamian.com/ 4. Cabot Technology Solutions Ohio-based Cabot Technology is a software development company that provides solutions for Healthcare. The company offers software for appointment making, health centre search, real-time monitoring, and more. Cabot also creates HIPAA-compliant Electronic Medical Record and Electronic Health Record solutions and builds medical devices. Highland Heights, OH Location: 2006 Founded: $10,000+ Min. Project Budget: Envision, ATMEL Wearable App, Erudyte Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://www.cabotsolutions.com/ 5. Velvetech Illinois-based Velvetech is an award-winning medical software company. It delivers the development of health monitoring solutions, medical education platforms, web/mobile applications for post-acute care coordination, practice management systems for medical practices, and more. Velvetech also empowers its healthcare software products with such emerging technologies as blockchain, artificial intelligence, and the internet of things. <a href="https://medium.com/media/64a5d44eb6bfcf5e7b17414514b50345/href">https://medium.com/media/64a5d44eb6bfcf5e7b17414514b50345/href</a> Northbrook, IL Location: 2004 Founded: $25,000+ Min. Project Budget: American Academy of Family Physicians, MedAdept, Healix Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://www.velvetech.com/ 6. IT Craft With more than 17 years of experience and 280 professionals aboard, IT Craft is a software development company that offers healthcare solutions. The company delivers the development of healthcare-related mobile/web applications, custom healthcare portals, IoT-powered medical solutions, electronic health record systems, data management systems, and more. Berlin, Germany Location: 2001 Founded: $10,000+ Min. Project Budget: Arctrieval, DRG Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://itechcraft.com/ 7. Seamgen Established in 2008, Seamgen is a California-based software development enterprise that offers healthcare applications development. It delivers solutions for e-prescribing, remote diagnosis, electronic medical records management, patient management portals, etc. Seamgen serves healthcare organizations of all sizes and specialities, from insurance teams to large hospitals. All of the company’s technical solutions are HIPAA-compliant. San Diego, CA Location: 2008 Founded: $75,000+ Min. Project Budget: DizzyDoctor, WellMed, WellRx Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://www.seamgen.com/ 8. SunNet Solutions Established in 2011, SunNet Solutions is an award-winning medical software company. SunNet delivers the development of a wide variety of healthcare-related software solutions, from tools for health progress monitoring to medical cases management systems. SunNet has delivered a large number of digital healthcare projects, including the end-to-end creation of FACN website and Act Early Texas website. Houston, TX Location: 2001 Founded: $25,000+ Min. Project Budget: FACN, Act Early Texas Major Healthcare Customers: 10–49 Average Number of Employees: Website: https://sunnet.us/ 9. Nisos Technologies Nisos Technologies is a healthcare software development company that delivers custom software development to healthcare providers, healthcare software organizations, and healthcare insurance firms. The company delivers the creation of the following healthcare software: clinic management software, online appointments systems, health and wellness software for employers, physician referral management systems, and more. Brooklyn, NY Location: 2012 Founded: $25,000+ Min. Project Budget: Magnacare, Brighton Health Plan, Mount Sinai Major Healthcare Customers: 10–49 Average Number of Employees: Website: https://nisostech.com/ 10. BVG Software Group Founded in 2011, California-based BVG Software Group is a company that delivers web and mobile solutions for various industries, including healthcare. The company provides healthcare web/mobile applications, digital healthcare solutions for patients, digital instruments for psychological parameters research and diagnosis, and more. South San Francisco, CA Location: 2011 Founded: $10,000+ Min. Project Budget: SkinData, Almed, Kisquali Games Major Healthcare Customers: 10–49 Average Number of Employees: Website: http://www.bvgsoftwaregroup.com/ <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

Top Healthcare Software Development Companies

As the metaverse develops, it presents opportunities for restaurants to improve critical areas of their business such as online ordering, loyalty programs, and menu management. Software companies can profit by helping restaurants integrate restaurant management software with metaverse-compatible technologies such as augmented reality and virtual reality (VR), the Internet of Things (IoT), blockchains, and cryptocurrencies. Those technologies can be used as part of online worlds and spaces that comprise the metaverse. A good example of the metaverse is , a selection of virtual reality worlds owned by Meta, the parent company of Facebook. These worlds can be explored with the Oculus Quest 2, a VR headset. Horizon Worlds Metaverse Software If you want to take your software company to the next level, it’s time for you to develop metaverse-powered software that will appeal to restaurants. There’s no time to waste, Restaurants are already exploring the use of that software. So, it’s best to find a software development company that offers metaverse development services. With access to those services, software proprietors can use metaverse technologies to enhance restaurant management software features. Those features can include online food ordering, customer loyalty and rewards modules, and staff management. Food Ordering in the Metaverse Restaurant management software platforms often include an online ordering module that is integrated with a point-of-sale (POS) system. This combination makes it possible to create a seamless, convenient digital food ordering experience for customers. Just imagine if you could extend the functionality of a restaurant software platform by bringing online into a metaverse world. It’s possible. food ordering services Metaverse food ordering capabilities can be created through the development of custom e-commerce shopping cart software and designs, payment gateway integration, and digital wallets. The wallets can store payment information from credit cards, debit cards, and bank accounts. A variation of the digital wallet is the crypto wallet, which can be used to store, send, and receive cryptocurrency for online payments. Allowing food to be ordered in the metaverse and delivered in the real world creates an engaging customer experience. That’s why integrating virtual reality and augmented reality (AR) software with restaurant management software would be so powerful. Experienced AR developers could build a metaverse-enabled mobile application incorporating unique 3D modeling capabilities for restaurant menu items. Customers could use the application in conjunction with AR glasses or smartphones to view three-dimensional displays of entrees, appetizers, and drinks. Few things will stimulate a person’s appetite faster than seeing a delicious plate of food. So, software companies have a unique opportunity to acquire restaurants as clients by developing mobile AR software applications that make food look enticing. Customer Loyalty and Rewards Customer loyalty programs help restaurants retain customers. These programs can be launched in online worlds that are part of the metaverse. Of course, customer loyalty and rewards software modules would have to be developed, and those modules would have to contain several important features: integration with POS systems, customer activity tracking, and social sharing. Social sharing features would enable restaurants to reward customers who share offers on social networks. NFTs in the Metaverse It’s also possible to use digital assets known as non-fungible tokens (NFTs) for restaurant loyalty programs. An NFT is a blockchain-based digital unit representing a unique asset such as collectible artwork, media, and real estate property rights. NFTs are unique cryptographic tokens that cannot be replicated. Offering NFT-based memberships can elevate the customer loyalty experience. And, a software development company can make those these memberships possible by building and integrating an NFT marketplace with customer loyalty software. It is on that marketplace that NFTs can be created, bought, and sold. A cryptocurrency wallet would have to be developed specifically for use with the marketplace. Of course, the wallet should store a cryptocurrency such as Ethereum. Customers will then be able to buy the restaurant’s NFTs with Ethereum. A good option is to make the wallet compatible with multiple cryptocurrencies. Doing so will ensure that customers have tremendous flexibility. Staff Management Staff management software helps with managing employee onboarding/offboarding, hours/timesheets, and payroll. Employee training, in particular, could be enhanced through the use of VR technology. VR would make a cost-effective and scalable addition to a restaurant’s training program. The VR software applications could be created with gaming engines such as Unreal Engine and Unity and would have to be compatible with popular VR headsets such as the Oculus Quest 2 and Samsung Gear VR. VR apps can then be integrated with human capital management (HCM)software modules that are used to manage employee training and information while maintaining labor law compliance. Restaurant Management Platform Integrated with Metaverse Software Software proprietors can help restaurants enhance their marketing, customer experience, and operations by implementing custom software solutions that integrate metaverse-compatible technology with a restaurant management platform. Online food ordering, customer loyalty programs, and staff management are all restaurant management software modules that could be taken to the next level. To provide these capabilities, software companies can use the services of an experienced software development company. That company can provide metaverse development services and would be proficient in blockchain technology, augmented and virtual reality, and 3D virtual design. The developers would also ensure that the metaverse network and all components within the network remain secure and in reliable working order through maintenance and upgrades.

Metaverse: The Next Game Changer For Restaurant Management 

Technology is relentless in forcing industries to evolve. Regarding big data, machine learning, and artificial intelligence, processing and analyzing massive volumes of real-time data has become a critical competitive advantage for all companies. However, building and maintaining reliable data platforms capable of handling such scale presents formidable challenges. Now more than ever, software engineers must be creative thinkers and innovators. Rahul Chaturvedi, a Staff Software Engineer at Uber Technologies Inc., is an excellent example of the problem-solver these challenges require. His innovative approaches to data infrastructure are meeting every future-forward standard in the tech industry today. With nearly two decades of experience in software engineering, Chaturvedi has established himself as an authority in big data, streaming technologies, and cloud infrastructure. His expertise, honed through roles at industry giants and bolstered by his academic achievements at IIT Kharagpur and the University of Washington, has been instrumental in tackling some of the most complex data challenges modern tech companies face. At Uber, Chaturvedi has been at the forefront of optimizing one of the world's largest Kafka deployments, a system that processes trillions of messages and petabytes of data daily. His work addresses the immediate needs of real-time data processing and lays the groundwork for robust AI ecosystems that can scale efficiently in cloud environments. The Scale of the Challenge: Uber's Data Ecosystem To truly appreciate the significance of Rahul Chaturvedi's contributions, one must first grasp the sheer scale of Uber's data ecosystem. As one of the world's leading transportation platforms, Uber's operations generate an astronomical volume of data: trillions of messages daily, amounting to petabytes of information that must be processed, analyzed, and acted upon in real-time. This data deluge stems from various sources: ride requests, driver locations, traffic conditions, payment transactions, and countless other data points from Uber's global network of services. Each data stream is critical for the company's operations, feeding into systems that power everything from dynamic pricing algorithms to route optimization and fraud detection. The complexity of Uber's data landscape is further compounded by several factors, including real-time processing requirements, the global distribution of data, seamless scalability without compromising performance or reliability, and cost efficiency. Chaturvedi's challenge was maintaining this system and enhancing its performance while preparing it for significant cloud migration. This required innovative approaches to long-standing problems in distributed systems and data engineering - solutions that would need to work at an unprecedented scale. Innovating on Solutions: Kafka Optimization Strategies Much of Rahul Chaturvedi's work at Uber was defined by his innovative approach to optimizing Kafka, a distributed streaming platform that forms the backbone of Uber's real-time data processing infrastructure. One of Chaturvedi's most significant contributions was leading the effort to co-locate Kafka with other technologies on the same host. Due to the critical nature of the technology, this move had never been attempted before at Uber. This strategy was crucial for enabling Uber's cloud migration while optimizing costs. Co-location introduced new challenges, particularly the "noisy neighbor" problem, where one service could potentially impact the performance of others on the same host. Other challenges included resource constraints and kernel version challenges, but Chaturvedi worked with various teams to solve these problems. The results of these optimization efforts were significant. Not only did they pave the way for a smooth cloud migration, but they also led to substantial improvements in system efficiency and reliability. The co-location strategy alone is estimated to save Uber millions of dollars annually in infrastructure costs. Future-Forward Outcomes: Building Robust AI Ecosystems Rahul Chaturvedi's work at Uber extends beyond optimizing Kafka and managing cloud migration. His efforts have been instrumental in building a robust ecosystem that supports Uber's extensive AI and machine learning initiatives. The optimized Kafka infrastructure allows for the ingestion and distribution of massive data streams necessary for training and feeding live data to AI models, and it can handle the varying and often unpredictable data volume requirements of different ML models and training processes. Besides Kafka, Chaturvedi's work involved integrating Redis into Uber's data infrastructure. This allows ML/AI teams to cache frequently accessed data or pre-processed features, significantly improving AI models' efficiency during training and inference. By strategically using Redis alongside Kafka, Chaturvedi's team created a system that provides ultra-low latency access to critical data essential for real-time AI applications. The smooth combination of Redis with an efficiently co-located Kafka infrastructure is just the kind of future-focused innovation that engineers should aspire to. Chaturvedi's work lays the foundation for future advancements in Uber's AI capabilities thanks to a data platform designed with the flexibility to incorporate new AI technologies and methodologies as they emerge. Chaturvedi has played a pivotal role in creating an ecosystem where AI and ML can flourish at Uber's massive scale through his work on Kafka, Redis, and the overall data infrastructure. Industry Impact: The Engineering of the Future Rahul Chaturvedi's contributions to Uber's data infrastructure have had far-reaching implications for the company and the broader tech industry. The co-location of Kafka with other technologies is projected to save Uber millions of dollars annually in infrastructure costs. Latency reduction in critical Kafka clusters has improved overall system responsiveness, directly impacting user experience. The optimized infrastructure can now handle Uber's massive daily data volumes - trillions of messages - more efficiently. The significance of Chaturvedi's work has not gone unnoticed. He has received three coveted "Search Storage Data (SSD) Impact Awards" in 2023 at Uber Technologies Inc., and his innovative approaches have garnered attention in the tech community, positioning him as a thought leader in big data and infrastructure. Rahul Chaturvedi's work at Uber is an excellent example of the critical role that innovative data engineering plays in modern technology companies. His strategies for building reliable, scalable, and efficient data platforms have solved immediate challenges and positioned Uber at the forefront of big data and AI technologies. Solutions like these, whether conducted on a massive scale like Uber or smaller scales by ambitious startups and small businesses, will inspire new technology and new strategies in future decades. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Technology is relentless in forcing industries to evolve. Regarding big data, machine learning, and artificial intelligence, processing and analyzing massive volumes of real-time data has become a critical competitive advantage for all companies. However, building and maintaining reliable data platforms capable of handling such scale presents formidable challenges. Now more than ever, software engineers must be creative thinkers and innovators. Rahul Chaturvedi , a Staff Software Engineer at Uber Technologies Inc., is an excellent example of the problem-solver these challenges require. His innovative approaches to data infrastructure are meeting every future-forward standard in the tech industry today. Rahul Chaturvedi Rahul Chaturvedi With nearly two decades of experience in software engineering, Chaturvedi has established himself as an authority in big data, streaming technologies, and cloud infrastructure. His expertise, honed through roles at industry giants and bolstered by his academic achievements at IIT Kharagpur and the University of Washington, has been instrumental in tackling some of the most complex data challenges modern tech companies face. At Uber, Chaturvedi has been at the forefront of optimizing one of the world's largest Kafka deployments, a system that processes trillions of messages and petabytes of data daily. His work addresses the immediate needs of real-time data processing and lays the groundwork for robust AI ecosystems that can scale efficiently in cloud environments. The Scale of the Challenge: Uber's Data Ecosystem To truly appreciate the significance of Rahul Chaturvedi's contributions, one must first grasp the sheer scale of Uber's data ecosystem. As one of the world's leading transportation platforms, Uber's operations generate an astronomical volume of data: trillions of messages daily, amounting to petabytes of information that must be processed, analyzed, and acted upon in real-time. This data deluge stems from various sources: ride requests, driver locations, traffic conditions, payment transactions, and countless other data points from Uber's global network of services. Each data stream is critical for the company's operations, feeding into systems that power everything from dynamic pricing algorithms to route optimization and fraud detection. The complexity of Uber's data landscape is further compounded by several factors, including real-time processing requirements, the global distribution of data, seamless scalability without compromising performance or reliability, and cost efficiency. Chaturvedi's challenge was maintaining this system and enhancing its performance while preparing it for significant cloud migration. This required innovative approaches to long-standing problems in distributed systems and data engineering - solutions that would need to work at an unprecedented scale. Innovating on Solutions: Kafka Optimization Strategies Much of Rahul Chaturvedi's work at Uber was defined by his innovative approach to optimizing Kafka, a distributed streaming platform that forms the backbone of Uber's real-time data processing infrastructure. One of Chaturvedi's most significant contributions was leading the effort to co-locate Kafka with other technologies on the same host. Due to the critical nature of the technology, this move had never been attempted before at Uber. This strategy was crucial for enabling Uber's cloud migration while optimizing costs. Co-location introduced new challenges, particularly the "noisy neighbor" problem, where one service could potentially impact the performance of others on the same host. Other challenges included resource constraints and kernel version challenges, but Chaturvedi worked with various teams to solve these problems. The results of these optimization efforts were significant. Not only did they pave the way for a smooth cloud migration, but they also led to substantial improvements in system efficiency and reliability. The co-location strategy alone is estimated to save Uber millions of dollars annually in infrastructure costs. Future-Forward Outcomes: Building Robust AI Ecosystems Rahul Chaturvedi's work at Uber extends beyond optimizing Kafka and managing cloud migration. His efforts have been instrumental in building a robust ecosystem that supports Uber's extensive AI and machine learning initiatives. The optimized Kafka infrastructure allows for the ingestion and distribution of massive data streams necessary for training and feeding live data to AI models, and it can handle the varying and often unpredictable data volume requirements of different ML models and training processes. Besides Kafka, Chaturvedi's work involved integrating Redis into Uber's data infrastructure. This allows ML/AI teams to cache frequently accessed data or pre-processed features, significantly improving AI models' efficiency during training and inference. By strategically using Redis alongside Kafka, Chaturvedi's team created a system that provides ultra-low latency access to critical data essential for real-time AI applications. The smooth combination of Redis with an efficiently co-located Kafka infrastructure is just the kind of future-focused innovation that engineers should aspire to. Chaturvedi's work lays the foundation for future advancements in Uber's AI capabilities thanks to a data platform designed with the flexibility to incorporate new AI technologies and methodologies as they emerge. Chaturvedi has played a pivotal role in creating an ecosystem where AI and ML can flourish at Uber's massive scale through his work on Kafka, Redis, and the overall data infrastructure. Industry Impact: The Engineering of the Future Rahul Chaturvedi's contributions to Uber's data infrastructure have had far-reaching implications for the company and the broader tech industry. The co-location of Kafka with other technologies is projected to save Uber millions of dollars annually in infrastructure costs. Latency reduction in critical Kafka clusters has improved overall system responsiveness, directly impacting user experience. The optimized infrastructure can now handle Uber's massive daily data volumes - trillions of messages - more efficiently. Rahul Chaturvedi's contributions Rahul Chaturvedi's contributions The significance of Chaturvedi's work has not gone unnoticed. He has received three coveted "Search Storage Data (SSD) Impact Awards" in 2023 at Uber Technologies Inc., and his innovative approaches have garnered attention in the tech community, positioning him as a thought leader in big data and infrastructure. Chaturvedi's work Chaturvedi's work Rahul Chaturvedi's work at Uber is an excellent example of the critical role that innovative data engineering plays in modern technology companies. His strategies for building reliable, scalable, and efficient data platforms have solved immediate challenges and positioned Uber at the forefront of big data and AI technologies. Solutions like these, whether conducted on a massive scale like Uber or smaller scales by ambitious startups and small businesses, will inspire new technology and new strategies in future decades. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure : This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure

Mastering Real-Time Data: Rahul Chaturvedi's Strategies for Building Reliable Data Platforms

রিয়েল-টাইম ডেটা আয়ত্ত করা: নির্ভরযোগ্য ডেটা প্ল্যাটফর্ম তৈরির জন্য রাহুল চতুর্বেদীর কৌশলগুলি

Echtzeitdaten beherrschen: Rahul Chaturvedis Strategien zum Aufbau zuverlässiger Datenplattformen

Dominar los datos en tiempo real: estrategias de Rahul Chaturvedi para crear plataformas de datos confiables

Maîtriser les données en temps réel : les stratégies de Rahul Chaturvedi pour créer des plates-formes de données fiables

रियल-टाइम डेटा में महारत हासिल करना: विश्वसनीय डेटा प्लेटफ़ॉर्म बनाने के लिए राहुल चतुर्वेदी की रणनीतियाँ

リアルタイム データの習得: 信頼性の高いデータ プラットフォームを構築するための Rahul Chaturvedi の戦略

실시간 데이터 마스터하기: 안정적인 데이터 플랫폼 구축을 위한 Rahul Chaturvedi의 전략

Dominando dados em tempo real: estratégias de Rahul Chaturvedi para construir plataformas de dados confiáveis

Освоение данных в реальном времени: стратегии Рахула Чатурведи по созданию надежных платформ данных

Gerçek Zamanlı Verilerde Uzmanlaşmak: Rahul Chaturvedi'nin Güvenilir Veri Platformları Oluşturma Stratejileri

Làm chủ dữ liệu thời gian thực: Chiến lược xây dựng nền tảng dữ liệu đáng tin cậy của Rahul Chaturvedi

掌握实时数据：Rahul Chaturvedi 构建可靠数据平台的策略

Blockchain platform Sui has announced integration with Bitcoin restaking platform SatLayer, marking an expansion of Bitcoin-based decentralized finance (BTCfi) capabilities. The integration, scheduled for December 18, 2024, builds upon Sui's previous Bitcoin staking implementation announced in November. The technical framework centers on SatLayer's deployment as smart contracts on the Babylon Chain, enabling programmable slashing conditions for Bitcoin assets. This system allows decentralized applications to utilize Bitcoin for security and liquidity purposes across various services, including bridges, exchanges, and oracle systems. Several projects within the Sui ecosystem plan to implement this technology, including Navi, Suilend, Cetus, Aftermath, and Ika. These implementations will focus on developing Bitcoin Validated Services (BVSes) within the Move programming language environment. The integration includes support for Bitcoin Liquid Staking Tokens (LSTs) through partnerships with financial protocols Lombard Finance and Lorenzo Protocol. This framework aims to provide applications built on Sui with access to Bitcoin's liquidity pool, estimated at nearly $2 trillion in market value. Technical implementation of the system involves multiple components: Bitcoin staking through Babylon's protocol


Restaking functionality via SatLayer


Smart contract deployment for programmable slashing


Integration with existing Sui-based applications


Implementation of liquid staking tokens The development represents a continuation of Sui's November announcement regarding Bitcoin staking capabilities, which involved collaboration between Babylon, Lombard Finance, and Cubist. SatLayer serves as Babylon's designated partner for restaking operations. From a technical perspective, the integration utilizes Sui's object-centric model and the Move programming language to enable parallel execution of transactions. This architecture aims to maintain sub-second transaction finality while incorporating Bitcoin's security model. The partnership forms part of a broader trend in blockchain development, where platforms seek to bridge traditional cryptocurrency assets with newer decentralized finance capabilities. This approach attempts to address limitations in Bitcoin's programmability while maintaining its security characteristics. Babylon's involvement brings additional technical capabilities through their Bitcoin staking protocol, which enables Bitcoin holders to participate in Proof-of-Stake systems without requiring third-party custody services. This self-custodial approach differentiates the implementation from traditional wrapped token solutions. The development team behind SatLayer includes MIT alumni, while Babylon's technical leadership includes Stanford Professor David Tse, known for his work in wireless communications algorithms. The technical implementation builds on research in consensus protocols and layer 1 blockchain architecture. This integration represents a significant technical development in the blockchain sector's efforts to expand Bitcoin's utility beyond its original design as a peer-to-peer electronic cash system. By enabling Bitcoin assets to interact with smart contract platforms, the system aims to increase the cryptocurrency's role in decentralized finance operations. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Blockchain platform Sui has announced integration with Bitcoin restaking platform SatLayer , marking an expansion of Bitcoin-based decentralized finance (BTCfi) capabilities. The integration, scheduled for December 18, 2024, builds upon Sui's previous Bitcoin staking implementation announced in November. The technical framework centers on SatLayer's deployment as smart contracts on the Babylon Chain, enabling programmable slashing conditions for Bitcoin assets. This system allows decentralized applications to utilize Bitcoin for security and liquidity purposes across various services, including bridges, exchanges, and oracle systems. Sui SatLayer announced Babylon Several projects within the Sui ecosystem plan to implement this technology, including Navi, Suilend, Cetus, Aftermath, and Ika. These implementations will focus on developing Bitcoin Validated Services (BVSes) within the Move programming language environment. The integration includes support for Bitcoin Liquid Staking Tokens (LSTs) through partnerships with financial protocols Lombard Finance and Lorenzo Protocol. This framework aims to provide applications built on Sui with access to Bitcoin's liquidity pool, estimated at nearly $2 trillion in market value. Technical implementation of the system involves multiple components: Bitcoin staking through Babylon's protocol Restaking functionality via SatLayer Smart contract deployment for programmable slashing Integration with existing Sui-based applications Implementation of liquid staking tokens Bitcoin staking through Babylon's protocol Bitcoin staking through Babylon's protocol Restaking functionality via SatLayer Restaking functionality via SatLayer Smart contract deployment for programmable slashing Smart contract deployment for programmable slashing Integration with existing Sui-based applications Integration with existing Sui-based applications Implementation of liquid staking tokens Implementation of liquid staking tokens The development represents a continuation of Sui's November announcement regarding Bitcoin staking capabilities, which involved collaboration between Babylon, Lombard Finance, and Cubist. SatLayer serves as Babylon's designated partner for restaking operations. From a technical perspective, the integration utilizes Sui's object-centric model and the Move programming language to enable parallel execution of transactions. This architecture aims to maintain sub-second transaction finality while incorporating Bitcoin's security model. The partnership forms part of a broader trend in blockchain development, where platforms seek to bridge traditional cryptocurrency assets with newer decentralized finance capabilities. This approach attempts to address limitations in Bitcoin's programmability while maintaining its security characteristics. Babylon's involvement brings additional technical capabilities through their Bitcoin staking protocol, which enables Bitcoin holders to participate in Proof-of-Stake systems without requiring third-party custody services. This self-custodial approach differentiates the implementation from traditional wrapped token solutions. The development team behind SatLayer includes MIT alumni, while Babylon's technical leadership includes Stanford Professor David Tse, known for his work in wireless communications algorithms. The technical implementation builds on research in consensus protocols and layer 1 blockchain architecture. This integration represents a significant technical development in the blockchain sector's efforts to expand Bitcoin's utility beyond its original design as a peer-to-peer electronic cash system. By enabling Bitcoin assets to interact with smart contract platforms, the system aims to increase the cryptocurrency's role in decentralized finance operations. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Sui Network Expands Bitcoin Integration Through SatLayer Partnership

Sui Network udvider Bitcoin-integration gennem SatLayer-partnerskab

Sui Network amplía la integración de Bitcoin a través de la asociación con SatLayer

Sui Network étend l'intégration de Bitcoin grâce au partenariat SatLayer

Sui Network espande l'integrazione di Bitcoin tramite la partnership SatLayer

Sui Network、SatLayerとの提携を通じてビットコインの統合を拡大

បណ្តាញ Sui ពង្រីកការរួមបញ្ចូល Bitcoin តាមរយៈភាពជាដៃគូ SatLayer

Réseau Sui Epanzi Intégration ya Bitcoin Na nzela ya Partenariat SatLayer

Ny Sui Network dia manitatra ny fampidirana Bitcoin amin'ny alàlan'ny fiaraha-miasa amin'ny SatLayer

Sui Llika Bitcoin Tinkiyta Mastarin SatLayer Huñunakuywan

Sieť Sui rozširuje integráciu bitcoinov prostredníctvom partnerstva SatLayer

Sui Network, SatLayer Ortaklığıyla Bitcoin Entegrasyonunu Genişletiyor

Sui Network SatLayer hamkorlik orqali Bitcoin integratsiyasini kengaytiradi

A Reference List to Learn More About Image Editing, Video Editing, and Diffusion Models

Table of Links Abstract and 1. Introduction 2 Related Work 3 Preliminaries 4 Method 4.1 Key Sample and Joint Editing 4.2 Edit Propagation Via TokenFlow 5 Results 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.3 Ablation Study 6 Discussion 7 Acknowledgement and References A Implementation Details 2 RELATED WORK Text-driven image & video synthesis Seminal works designed GAN architectures to synthesize images conditioned on text embeddings (Reed et al., 2016; Zhang et al., 2016). With the evergrowing scale of vision-language datasets and pretraining strategies (Radford et al., 2021; Schuhmann et al., 2022), there has been a remarkable progress in text-driven image generation capabilities. Users can sytnesize high-quality visual content using simple text prompts. Much of this progress is also attributed to diffusion models (Sohl-Dickstein et al., 2015; Croitoru et al., 2022; Dhariwal & Nichol, 2021; Ho et al., 2020; Nichol & Dhariwal, 2021) which have been established as stateof-the-art text-to-image generators (Nichol et al., 2021; Saharia et al., 2022; Ramesh et al., 2022; Rombach et al., 2022; Sheynin et al., 2022; Bar-Tal et al., 2023). Such models have been extended for text-to-video generation, by extending 2D architectures to the temporal dimension (e.g., using temporal attention Ho et al. (2022b)) and performing large-scale training on video datasets (Ho et al., 2022a; Blattmann et al., 2023; Singer et al., 2022). Recently, Gen-1 (Esser et al., 2023) tailored a diffusion model architecture for the task of video editing, by conditioning the network on structure/appearance representations. Nevertheless, due to their extensive computation and memory requirements, existing video diffusion models are still in infancy and are largely restricted to short clips, or exhibit lower visual quality compared to image models. On the other side of the spectrum, a promising recent trend of works leverage a pre-trained image diffusion model for video synthesis tasks, without additional training (Fridman et al., 2023; Wu et al., 2022; Lee et al., 2023a; Qi et al., 2023). Our work falls into this category, employing a pretrained text-to-image diffusion model for the task of video editing, without any training or finetuning. Consistent video stylization A common approach for video stylization involves applying image editing techniques (e.g., style transfer) on a frame-by-frame basis, followed by a post-processing stage to address temporal inconsistencies in the edited video (Lai et al. (2018b); Lei et al. (2020; 2023)). Although these methods effectively reduce high-frequency temporal flickering, they are not designed to handle frames that exhibit substantial variations in content, which often occur when applying text-based image editing techniques (Qi et al., 2023). Kasten et al. (2021) propose to decompose a video into a set of 2D atlases, each provides a unified representation of the background or of a foreground object throughout the video. Edits applied to the 2D atlases are automatically mapped back to the video, thus achieving temporal consistency with minimal effort. Bar-Tal et al. (2022); Lee et al. (2023b) leverage this representation to perform text-driven editing. However, the atlas representation is limited to videos with simple motion and requires long training, limiting the applicability of this technique and of the methods built upon it. Our work is also related to classical works that demonstrated that small patches in a natural video extensively repeat across frames (Shahar et al., 2011; Cheung et al., 2005), and thus consistent editing can by simplified by editing a subset of keyframes and propagating the edit across the video by establishing patch correspondences using handcrafted features and optical flow (Ruder et al., 2016; Jamriska et al., 2019) or by ˇ training a patch-based GAN (Texler et al., 2020). Nevertheless, such propagation methods struggle to handle videos with illumination changes, or with complex dynamics. Importantly, they rely on a user provided consistent edit of the keyframes, which remains a labor-intensive task yet to be automated. Yang et al. (2023) combines keyframe editing with a propagation method by Jamriska ˇ et al. (2019). They edit keyframes using a text-to-image diffusion model while enforcing optical flow constraints on the edited keyframes. However, since optical flow estimation between distant frames is not reliable, their method fails to consistently edit keyframes that are far apart (as seen in our Supplementary Material - SM), and as a result, fails to consistently edit most videos. Source Target I Target II Reconstructed Target (a) Warped Source Target (b) Nearest-Neighbour Field (c) Figure 2: Fine-grained feature correspondences. Features (i.e., output tokens from the self-attention modules) extracted from of a source frame are used to reconstruct nearby frames. This is done by: (a) swapping each feature in the target by its nearest feature in the source, in all layers and all generation time steps, and (b) simple warping in RGB space, using a nearest neighbour field (c), computed between the source and target features extracted from the highest resolution decoder layer. The target is faithfully reconstructed, demonstrating the high level of spatial granularity and shared content between the features. Our work shares a similar motivation as this approach that benefits from the temporal redundancies in natural videos. We show that such redundancies are also present in the feature space of a text-to-image diffusion model, and leverage this property to achieve consistency. Controlled generation via diffusion features manipulation Recently, a surge of works demonstrated how text-to-image diffusion models can be readily adapted to various editing and generation tasks, by performing simple operations on the intermediate feature representation of the diffusion network (Chefer et al., 2023; Hong et al., 2022; Ma et al., 2023; Tumanyan et al., 2023; Hertz et al., 2022; Patashnik et al., 2023; Cao et al., 2023). Luo et al. (2023); Zhang et al. (2023) demonstrated semantic appearance swapping using diffusion feature correspondences. Hertz et al. (2022) observed that by manipulating the cross-attention layers, it is possible to control the relation between the spatial layout of the image to each word in the text. Plugand-Play Diffusion (PnP, Tumanyan et al. (2023)) analyzed the spatial features and the self-attention maps and found that they capture semantic information at high spatial granularity. Tune-A-Video (Wu et al., 2022) observed that by extending the self-attention module to operate on more than a single frame, it is possible to generate frames that share a common global appearance. Qi et al. (2023); Ceylan et al. (2023); Khachatryan et al. (2023a); Shin et al. (2023); Liu et al. (2023) leverage this property to achieve globally-coherent video edits. Nevertheless, as demonstrated in Sec. 5, inflating the self-attention module is insufficient for achieving fine-grained temporal consistency. Prior and concurrent works either compromise visual quality, or exhibit limited temporal consistency. In this work, we also perform video editing via simple operations in the feature space of a pre-trained text-to-image model, we explicitly encourage the features of the model to be temporally consistent through TokenFlow. This paper is available on arxiv under CC BY 4.0 DEED DEED license. Authors:
(1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution;
(2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution;
(3) Shai Bagon, Weizmann Institute of Science;
(4) Tali Dekel, Weizmann Institute of Science. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Method 4 Method 4.1 Key Sample and Joint Editing 4.1 Key Sample and Joint Editing 4.2 Edit Propagation Via TokenFlow 4.2 Edit Propagation Via TokenFlow 5 Results 5 Results 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.3 Ablation Study 5.3 Ablation Study 6 Discussion 6 Discussion 7 Acknowledgement and References 7 Acknowledgement and References A Implementation Details A Implementation Details 2 RELATED WORK Text-driven image & video synthesis Seminal works designed GAN architectures to synthesize images conditioned on text embeddings (Reed et al., 2016; Zhang et al., 2016). With the evergrowing scale of vision-language datasets and pretraining strategies (Radford et al., 2021; Schuhmann et al., 2022), there has been a remarkable progress in text-driven image generation capabilities. Users can sytnesize high-quality visual content using simple text prompts. Much of this progress is also attributed to diffusion models (Sohl-Dickstein et al., 2015; Croitoru et al., 2022; Dhariwal & Nichol, 2021; Ho et al., 2020; Nichol & Dhariwal, 2021) which have been established as stateof-the-art text-to-image generators (Nichol et al., 2021; Saharia et al., 2022; Ramesh et al., 2022; Rombach et al., 2022; Sheynin et al., 2022; Bar-Tal et al., 2023). Such models have been extended for text-to-video generation, by extending 2D architectures to the temporal dimension (e.g., using temporal attention Ho et al. (2022b)) and performing large-scale training on video datasets (Ho et al., 2022a; Blattmann et al., 2023; Singer et al., 2022). Recently, Gen-1 (Esser et al., 2023) tailored a diffusion model architecture for the task of video editing, by conditioning the network on structure/appearance representations. Nevertheless, due to their extensive computation and memory requirements, existing video diffusion models are still in infancy and are largely restricted to short clips, or exhibit lower visual quality compared to image models. On the other side of the spectrum, a promising recent trend of works leverage a pre-trained image diffusion model for video synthesis tasks, without additional training (Fridman et al., 2023; Wu et al., 2022; Lee et al., 2023a; Qi et al., 2023). Our work falls into this category, employing a pretrained text-to-image diffusion model for the task of video editing, without any training or finetuning. Text-driven image & video synthesis Consistent video stylization A common approach for video stylization involves applying image editing techniques (e.g., style transfer) on a frame-by-frame basis, followed by a post-processing stage to address temporal inconsistencies in the edited video (Lai et al. (2018b); Lei et al. (2020; 2023)). Although these methods effectively reduce high-frequency temporal flickering, they are not designed to handle frames that exhibit substantial variations in content, which often occur when applying text-based image editing techniques (Qi et al., 2023). Kasten et al. (2021) propose to decompose a video into a set of 2D atlases, each provides a unified representation of the background or of a foreground object throughout the video. Edits applied to the 2D atlases are automatically mapped back to the video, thus achieving temporal consistency with minimal effort. Bar-Tal et al. (2022); Lee et al. (2023b) leverage this representation to perform text-driven editing. However, the atlas representation is limited to videos with simple motion and requires long training, limiting the applicability of this technique and of the methods built upon it. Our work is also related to classical works that demonstrated that small patches in a natural video extensively repeat across frames (Shahar et al., 2011; Cheung et al., 2005), and thus consistent editing can by simplified by editing a subset of keyframes and propagating the edit across the video by establishing patch correspondences using handcrafted features and optical flow (Ruder et al., 2016; Jamriska et al., 2019) or by ˇ training a patch-based GAN (Texler et al., 2020). Nevertheless, such propagation methods struggle to handle videos with illumination changes, or with complex dynamics. Importantly, they rely on a user provided consistent edit of the keyframes, which remains a labor-intensive task yet to be automated. Yang et al. (2023) combines keyframe editing with a propagation method by Jamriska ˇ et al. (2019). They edit keyframes using a text-to-image diffusion model while enforcing optical flow constraints on the edited keyframes. However, since optical flow estimation between distant frames is not reliable, their method fails to consistently edit keyframes that are far apart (as seen in our Supplementary Material - SM), and as a result, fails to consistently edit most videos. Source Target I Target II Reconstructed Target (a) Warped Source Target (b) Nearest-Neighbour Field (c) Figure 2: Fine-grained feature correspondences. Features (i.e., output tokens from the self-attention modules) extracted from of a source frame are used to reconstruct nearby frames. This is done by: (a) swapping each feature in the target by its nearest feature in the source, in all layers and all generation time steps, and (b) simple warping in RGB space, using a nearest neighbour field (c), computed between the source and target features extracted from the highest resolution decoder layer. The target is faithfully reconstructed, demonstrating the high level of spatial granularity and shared content between the features. Our work shares a similar motivation as this approach that benefits from the temporal redundancies in natural videos. We show that such redundancies are also present in the feature space of a text-to-image diffusion model, and leverage this property to achieve consistency. Consistent video stylization Controlled generation via diffusion features manipulation Recently, a surge of works demonstrated how text-to-image diffusion models can be readily adapted to various editing and generation tasks, by performing simple operations on the intermediate feature representation of the diffusion network (Chefer et al., 2023; Hong et al., 2022; Ma et al., 2023; Tumanyan et al., 2023; Hertz et al., 2022; Patashnik et al., 2023; Cao et al., 2023). Luo et al. (2023); Zhang et al. (2023) demonstrated semantic appearance swapping using diffusion feature correspondences. Hertz et al. (2022) observed that by manipulating the cross-attention layers, it is possible to control the relation between the spatial layout of the image to each word in the text. Plugand-Play Diffusion (PnP, Tumanyan et al. (2023)) analyzed the spatial features and the self-attention maps and found that they capture semantic information at high spatial granularity. Tune-A-Video (Wu et al., 2022) observed that by extending the self-attention module to operate on more than a single frame, it is possible to generate frames that share a common global appearance. Qi et al. (2023); Ceylan et al. (2023); Khachatryan et al. (2023a); Shin et al. (2023); Liu et al. (2023) leverage this property to achieve globally-coherent video edits. Nevertheless, as demonstrated in Sec. 5, inflating the self-attention module is insufficient for achieving fine-grained temporal consistency. Prior and concurrent works either compromise visual quality, or exhibit limited temporal consistency. In this work, we also perform video editing via simple operations in the feature space of a pre-trained text-to-image model, we explicitly encourage the features of the model to be temporally consistent through TokenFlow. Controlled generation via diffusion features manipulation This paper is available on arxiv under CC BY 4.0 DEED DEED license. This paper is available on arxiv under CC BY 4.0 DEED DEED license. available on arxiv Authors: (1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution; (2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution; (3) Shai Bagon, Weizmann Institute of Science; (4) Tali Dekel, Weizmann Institute of Science. Authors: Authors: (1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution; (2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution; (3) Shai Bagon, Weizmann Institute of Science; (4) Tali Dekel, Weizmann Institute of Science.

CHETU

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CHETU

chetu.com

1K-5K employees

Since 2000

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CHETU

EVERGREEN INDEX #3619

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