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 https://iscas3dv.github.io/deformshape

翻译：学习具有稠密对应的可变形物体三维形状表征是计算机视觉中的基础问题。现有方法通常需要特定语义领域的额外标注（如人体或动物的骨架姿态），这需要额外标注工作且存在误差累积问题，同时局限于特定领域。本文提出一种新颖的自监督方法，用于学习可变形物体的神经隐式形状表征，该方法可通过模板形状和三维稠密对应来表示形状。我们的方法无需骨架和蒙皮权重先验，仅需一组以有符号距离场表示的形状集合。为处理大幅度变形，我们约束学习得到的模板形状与训练形状处于同一隐空间，设计了一种新的局部刚性约束方法——在局部区域强制执行刚性变换并解决局部反射问题，同时提出层级刚性约束以减少模板形状与对应关系联合学习中的歧义性。大量实验表明，我们的模型能够表征具有大幅变形的形状。我们还展示了该形状表征可支持纹理迁移和形状编辑等典型应用，并取得了具有竞争力的性能。代码和模型已开源至 https://iscas3dv.github.io/deformshape