FaceFolds：用于动态人脸高效体积渲染的网格化辐射流形
FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic Faces
AI总结：
提出FaceFolds表示方法，通过学习离散辐射流形和单层网格实现动态人脸的高效体积渲染，在游戏引擎中达到媲美SOTA神经渲染的真实感与前所未有的帧率。
摘要：
动态人脸捕捉的3D渲染是一个极具挑战性的问题，需要在多个方面进行改进——照片级真实感、效率、兼容性和可配置性。我们提出了一种新颖的表示方法，能够以极少的计算和内存开销，实现演员动态面部表演的高质量体积渲染。该方法原生运行于商用图形软硬件上，并允许在质量和效率之间进行平滑权衡。我们的方法利用了神经渲染的最新进展，特别是通过学习离散辐射流形来稀疏采样场景以建模体积效果。我们通过为整个动态序列学习单一流形集合来实现高效建模，同时将外观变化隐式建模为时间规范纹理。我们导出单个分层网格和与视角无关的RGBA纹理视频，无需额外的ML集成即可与传统图形渲染器兼容。我们通过在游戏引擎中渲染真实演员的动态人脸捕捉来验证该方法，其照片级真实感可与最先进的神经渲染技术相媲美，且达到了前所未有的帧率。
英文摘要：
3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts$\unicode{x2014}$photorealism, efficiency, compatibility, and configurability. We present a novel representation that enables high-quality volumetric rendering of an actor's dynamic facial performances with minimal compute and memory footprint. It runs natively on commodity graphics soft- and hardware, and allows for a graceful trade-off between quality and efficiency. Our method utilizes recent advances in neural rendering, particularly learning discrete radiance manifolds to sparsely sample the scene to model volumetric effects. We achieve efficient modeling by learning a single set of manifolds for the entire dynamic sequence, while implicitly modeling appearance changes as temporal canonical texture. We export a single layered mesh and view-independent RGBA texture video that is compatible with legacy graphics renderers without additional ML integration. We demonstrate our method by rendering dynamic face captures of real actors in a game engine, at comparable photorealism to state-of-the-art neural rendering techniques at previously unseen frame rates.
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