AI-Mediated 3D Video Conferencing

We present an AI-mediated 3D video conferencing system that can reconstruct and autostereoscopically display a life-sized talking head using consumer-grade compute resources and minimal capture equipment. Our 3D capture uses a novel 3D lifting method that encodes a given 2D input into an efficient triplanar neural representation of the user, which can be rendered from novel viewpoints in real-time. Our AI-based techniques drastically reduce the cost for 3D capture, while providing a high-fidelity 3D representation on the receiver's end at the cost of traditional 2D video streaming. Additional advantages of our AI-based approach include the ability to accommodate both photorealistic and stylized avatars, and the ability to enable mutual eye contact in multi-directional video conferencing. We demonstrate our system using a tracked stereo display for a personal viewing experience as well as a light field display for a multi-viewer experience.


Alex Trevithick (US San Diego)
Shengze Wang (UNC Chapel Hill)
Mayoore Jaiswal (NVIDIA)

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