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Some of the most exciting experiences that Metaverse promises to offer, forinstance, live interactions with virtual characters in virtual environments,require real-time photo-realistic rendering. 3D reconstruction approaches torendering, active or passive, still require extensive cleanup work to fix themeshes or point clouds. In this paper, we present a neural volumographytechnique called neural volumetric video or NeuVV to support immersive,interactive, and spatial-temporal rendering of volumetric video contents withphoto-realism and in real-time. The core of NeuVV is to efficiently encode adynamic neural radiance field (NeRF) into renderable and editable primitives.We introduce two types of factorization schemes: a hyper-spherical harmonics(HH) decomposition for modeling smooth color variations over space and time anda learnable basis representation for modeling abrupt density and color changescaused by motion. NeuVV factorization can be integrated into a Video Octree(VOctree) analogous to PlenOctree to significantly accelerate training whilereducing memory overhead. Real-time NeuVV rendering further enables a class ofimmersive content editing tools. Specifically, NeuVV treats each VOctree as aprimitive and implements volume-based depth ordering and alpha blending torealize spatial-temporal compositions for content re-purposing. For example, wedemonstrate positioning varied manifestations of the same performance atdifferent 3D locations with different timing, adjusting color/texture of theperformer's clothing, casting spotlight shadows and synthesizing distancefalloff lighting, etc, all at an interactive speed. We further develop a hybridneural-rasterization rendering framework to support consumer-level VR headsetsso that the aforementioned volumetric video viewing and editing, for the firsttime, can be conducted immersively in virtual 3D space.

NeuVV: Neural Volumetric Videos with Immersive Rendering and Editing