It is extremely challenging to create an animatable clothed human avatar from
RGB videos, especially for loose clothes due to the difficulties in motion
modeling. To address this problem, we introduce a novel representation on the
basis of recent neural scene rendering techniques. The core of our
representation is a set of structured local radiance fields, which are anchored
to the pre-defined nodes sampled on a statistical human body template. These
local radiance fields not only leverage the flexibility of implicit
representation in shape and appearance modeling, but also factorize cloth
deformations into skeleton motions, node residual translations and the dynamic
detail variations inside each individual radiance field. To learn our
representation from RGB data and facilitate pose generalization, we propose to
learn the node translations and the detail variations in a conditional
generative latent space. Overall, our method enables automatic construction of
animatable human avatars for various types of clothes without the need for
scanning subject-specific templates, and can generate realistic images with
dynamic details for novel poses. Experiment show that our method outperforms
state-of-the-art methods both qualitatively and quantitatively.

使用基于神经场景渲染技术的新型表示方法，将结构化本地辐射场锚定到人体模板节点上，将服装变形分解为骨架运动、节点残差翻译和每个辐射场内的动态细节变化，支持各种服装类型的自动构建可动画人形化身，并在新姿势下生成真实的动态细节图像。