Content material streaming and engagement are getting into a brand new dimension with QUEEN, an AI mannequin by NVIDIA Analysis and the College of Maryland that makes it doable to stream free-viewpoint video, which lets viewers expertise a 3D scene from any angle.
QUEEN could possibly be used to construct immersive streaming purposes that train abilities like cooking, put sports activities followers on the sector to look at their favourite groups play from any angle, or deliver an additional degree of depth to video conferencing within the office. It may be utilized in industrial environments to assist teleoperate robots in a warehouse or a producing plant.
The mannequin might be introduced at NeurIPS, the annual convention for AI analysis that begins Tuesday, Dec. 10, in Vancouver.
“To stream free-viewpoint movies in close to actual time, we should concurrently reconstruct and compress the 3D scene,” mentioned Shalini De Mello, director of analysis and a distinguished analysis scientist at NVIDIA. “QUEEN balances elements together with compression price, visible high quality, encoding time and rendering time to create an optimized pipeline that units a brand new commonplace for visible high quality and streamability.”
Cut back, Reuse and Recycle for Environment friendly Streaming
Free-viewpoint movies are sometimes created utilizing video footage captured from completely different digital camera angles, like a multicamera movie studio setup, a set of safety cameras in a warehouse or a system of videoconferencing cameras in an workplace.
Prior AI strategies for producing free-viewpoint movies both took an excessive amount of reminiscence for livestreaming or sacrificed visible high quality for smaller file sizes. QUEEN balances each to ship high-quality visuals — even in dynamic scenes that includes sparks, flames or furry animals — that may be simply transmitted from a number server to a shopper’s machine. It additionally renders visuals quicker than earlier strategies, supporting streaming use circumstances.
In most real-world environments, many components of a scene keep static. In a video, meaning a big share of pixels don’t change from one body to a different. To save lots of computation time, QUEEN tracks and reuses renders of those static areas — focusing as a substitute on reconstructing the content material that adjustments over time.
Utilizing an NVIDIA Tensor Core GPU, the researchers evaluated QUEEN’s efficiency on a number of benchmarks and located the mannequin outperformed state-of-the-art strategies for on-line free-viewpoint video on a spread of metrics. Given 2D movies of the identical scene captured from completely different angles, it sometimes takes below 5 seconds of coaching time to render free-viewpoint movies at round 350 frames per second.
This mixture of pace and visible high quality can assist media broadcasts of concert events and sports activities video games by providing immersive digital actuality experiences or prompt replays of key moments in a contest.
In warehouse settings, robotic operators may use QUEEN to raised gauge depth when maneuvering bodily objects. And in a videoconferencing utility — such because the 3D videoconferencing demo proven at SIGGRAPH and NVIDIA GTC — it may assist presenters reveal duties like cooking or origami whereas letting viewers decide the visible angle that greatest helps their studying.
The code for QUEEN will quickly be launched as open supply and shared on the undertaking web page.
QUEEN is one in all over 50 NVIDIA-authored NeurIPS posters and papers that function groundbreaking AI analysis with potential purposes in fields together with simulation, robotics and healthcare.
Generative Adversarial Nets, the paper that first launched GAN fashions, received the NeurIPS 2024 Check of Time Award. Cited greater than 85,000 instances, the paper was coauthored by Bing Xu, distinguished engineer at NVIDIA. Hear extra from its lead creator, Ian Goodfellow, analysis scientist at DeepMind, on the AI Podcast:
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