论文标题
CADEX：学习规范变形坐标空间，用于通过神经同构的动态表面表示
CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphism
论文作者
论文摘要
尽管对静态3D形状的神经表示进行了广泛的研究，但可变形表面的表示形式仅限于模板依赖性或缺乏效率。我们引入了典型的变形坐标空间（CADEX），这是形状和非辅助运动的统一表示。我们的关键见解是通过连续的两种构图图（同构）及其经过学习的规范形状的框架之间的变形分解。我们的新型变形表示及其实施是简单，有效的，并且保证了周期的一致性，拓扑保存，并在需要时进行体积保护。我们对学习规范形状的建模为先验学习提供了一个灵活稳定的空间。我们在建模各种可变形的几何形状时展示了最先进的性能：人体，动物身体和铰接物体。
While neural representations for static 3D shapes are widely studied, representations for deformable surfaces are limited to be template-dependent or lack efficiency. We introduce Canonical Deformation Coordinate Space (CaDeX), a unified representation of both shape and nonrigid motion. Our key insight is the factorization of the deformation between frames by continuous bijective canonical maps (homeomorphisms) and their inverses that go through a learned canonical shape. Our novel deformation representation and its implementation are simple, efficient, and guarantee cycle consistency, topology preservation, and, if needed, volume conservation. Our modelling of the learned canonical shapes provides a flexible and stable space for shape prior learning. We demonstrate state-of-the-art performance in modelling a wide range of deformable geometries: human bodies, animal bodies, and articulated objects.
