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Meeting online is becoming the new normal. Creating an immersive experiencefor online meetings is a necessity towards more diverse and seamlessenvironments. Efficient photorealistic rendering of human 3D dynamics is thecore of immersive meetings. Current popular applications achieve real-timeconferencing but fall short in delivering photorealistic human dynamics, eitherdue to limited 2D space or the use of avatars that lack realistic interactionsbetween participants. Recent advances in neural rendering, such as the NeuralRadiance Field (NeRF), offer the potential for greater realism in metaversemeetings. However, the slow rendering speed of NeRF poses challenges forreal-time conferencing. We envision a pipeline for a future extended realitymetaverse conferencing system that leverages monocular video acquisition andfree-viewpoint synthesis to enhance data and hardware efficiency. Towards animmersive conferencing experience, we explore an accelerated NeRF-basedfree-viewpoint synthesis algorithm for rendering photorealistic human dynamicsmore efficiently. We show that our algorithm achieves comparable renderingquality while performing training and inference 44.5% and 213% faster thanstate-of-the-art methods, respectively. Our exploration provides a design basisfor constructing metaverse conferencing systems that can handle complexapplication scenarios, including dynamic scene relighting with customizedthemes and multi-user conferencing that harmonizes real-world people into anextended world.

Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering