Our goal is to efficiently learn personalized animatable 3D head avatars from
videos that are geometrically accurate, realistic, relightable, and compatible
with current rendering systems. While 3D meshes enable efficient processing and
are highly portable, they lack realism in terms of shape and appearance. Neural
representations, on the other hand, are realistic but lack compatibility and
are slow to train and render. Our key insight is that it is possible to
efficiently learn high-fidelity 3D mesh representations via differentiable
rendering by exploiting highly-optimized methods from traditional computer
graphics and approximating some of the components with neural networks. To that
end, we introduce \moniker, a technique that enables the creation of animatable
and relightable mesh avatars from a single monocular video. First, we learn a
canonical geometry using a mesh representation, enabling efficient
differentiable rasterization and straightforward animation via learned
blendshapes and linear blend skinning weights. Second, we follow
physically-based rendering and factor observed colors into intrinsic albedo,
roughness, and a neural representation of the illumination, allowing the
learned avatars to be relit in novel scenes. Since our input videos are
captured on a single device with a narrow field of view, modeling the
surrounding environment light is non-trivial. Based on the split-sum
approximation for modeling specular reflections, we address this by
approximating the pre-filtered environment map with a multi-layer perceptron
(MLP) modulated by the surface roughness, eliminating the need to explicitly
model the light. We demonstrate that our mesh-based avatar formulation,
combined with learned deformation, material, and lighting MLPs, produces
avatars with high-quality geometry and appearance, while also being efficient
to train and render compared to existing approaches.

我们提出了一种从单一单目视频中学习可个性化动画的、具有几何精度、逼真性、可重新照明性并且与当前渲染系统兼容的 3D 头像人物模型的高效方法。