We present a novel neural implicit representation for articulated human
bodies. Compared to explicit template meshes, neural implicit body
representations provide an efficient mechanism for modeling interactions with
the environment, which is essential for human motion reconstruction and
synthesis in 3D scenes. However, existing neural implicit bodies suffer from
either poor generalization on highly articulated poses or slow inference time.
In this work, we observe that prior knowledge about the human body's shape and
kinematic structure can be leveraged to improve generalization and efficiency.
We decompose the full-body geometry into local body parts and employ a
part-aware encoder-decoder architecture to learn neural articulated occupancy
that models complex deformations locally. Our local shape encoder represents
the body deformation of not only the corresponding body part but also the
neighboring body parts. The decoder incorporates the geometric constraints of
local body shape which significantly improves pose generalization. We
demonstrate that our model is suitable for resolving self-intersections and
collisions with 3D environments. Quantitative and qualitative experiments show
that our method largely outperforms existing solutions in terms of both
efficiency and accuracy. The code and models are available at
this https URL

本研究提出了一种新型的神经隐式表示方法，旨在有效地对人体的关节运动进行建模、重构和合成，该方法利用具有局部感知能力的编码器 - 解码器结构将全身几何形状分解成局部身体部位，并学习局部复杂变形的神经隐式占据，同时考虑到身体部位的几何限制来提高姿态泛化能力，在 3D 环境中易于处理自相交和碰撞，实验结果表明，该模型在效率和准确性方面明显优于现有的解决方案。