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.

我们提出了一种新颖的表示方法，可以在演员的动态面部表现中实现高质量的体渲染，同时具有较小的计算和内存占用，能够以最佳的质量和效率进行权衡。我们的方法利用了神经渲染的最新进展，在模型体积效果时通过学习离散辐射流形进行场景稀疏采样。我们通过学习单个流形集对整个动态序列进行了高效建模，并通过时态规范纹理隐式建模外貌变化。我们导出了一个与传统图形渲染器兼容的单层网格和视角无关的 RGBA 纹理视频，通过在游戏引擎中渲染真实演员的动态面部捕捉，实现与最先进的神经渲染技术相媲美的逼真度，同时具备之前无法达到的帧率。