Learning 3D shape representation with dense correspondence for deformable
objects is a fundamental problem in computer vision. Existing approaches often
need additional annotations of specific semantic domain, e.g., skeleton poses
for human bodies or animals, which require extra annotation effort and suffer
from error accumulation, and they are limited to specific domain. In this
paper, we propose a novel self-supervised approach to learn neural implicit
shape representation for deformable objects, which can represent shapes with a
template shape and dense correspondence in 3D. Our method does not require the
priors of skeleton and skinning weight, and only requires a collection of
shapes represented in signed distance fields. To handle the large deformation,
we constrain the learned template shape in the same latent space with the
training shapes, design a new formulation of local rigid constraint that
enforces rigid transformation in local region and addresses local reflection
issue, and present a new hierarchical rigid constraint to reduce the ambiguity
due to the joint learning of template shape and correspondences. Extensive
experiments show that our model can represent shapes with large deformations.
We also show that our shape representation can support two typical
applications, such as texture transfer and shape editing, with competitive
performance. The code and models are available at
this https URL

使用自监督学习方法为可变形物体学习神经隐含形状表示法，无需特定语义领域的额外注释和大量变形约束，可用于纹理转移和形状编辑应用。