We present Neural Shape Deformation Priors, a novel method for shape manipulation that predicts mesh deformations of non-rigid objects from user-provided handle movements. State-of-the-art methods cast this problem as an optimization task, where the input source mesh is iteratively deformed to minimize an objective function according to hand-crafted regularizers such as ARAP. In this work, we learn the deformation behavior based on the underlying geometric properties of a shape, while leveraging a large-scale dataset containing a diverse set of non-rigid deformations. Specifically, given a source mesh and desired target locations of handles that describe the partial surface deformation, we predict a continuous deformation field that is defined in 3D space to describe the space deformation. To this end, we introduce transformer-based deformation networks that represent a shape deformation as a composition of local surface deformations. It learns a set of local latent codes anchored in 3D space, from which we can learn a set of continuous deformation functions for local surfaces. Our method can be applied to challenging deformations and generalizes well to unseen deformations. We validate our approach in experiments using the DeformingThing4D dataset, and compare to both classic optimization-based and recent neural network-based methods.

翻译：我们提出神经形状变形先验（Neural Shape Deformation Priors），一种新颖的形状操控方法，能够根据用户提供的控制手柄运动预测非刚性物体的网格变形。现有最先进方法将此问题视为优化任务，通过迭代变形输入源网格，依据手工设计的正则化项（如ARAP）最小化目标函数。在本工作中，我们基于形状的底层几何属性学习变形行为，同时利用包含多种非刚性变形的大规模数据集。具体而言，给定源网格及描述局部表面变形的目标手柄位置，我们预测连续变形场，该场在三维空间中定义以描述空间变形。为此，我们引入基于Transformer的变形网络，将形状变形表示为局部表面变形的组合。该网络学习一组锚定于三维空间的局部潜编码，据此可学习局部表面的连续变形函数。我们的方法能够处理复杂变形，并对未见变形具有良好泛化性。我们使用DeformingThing4D数据集进行实验验证，并与经典优化方法和近期基于神经网络的方法进行对比。