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:我们展示了神经形状变形前哨, 这是一种新颖的形状操纵方法, 预测用户提供的控件运动中非硬性物体的网状变形。 最先进的方法将这一问题作为一种优化任务, 输入源网被迭代变形, 以根据手工制作的调节器( 如 APAP) 最大限度地减少客观功能。 在这项工作中, 我们学习基于形状基本几何特性的变形行为, 同时利用一个大型数据集, 其中包含一系列非硬性变形的多种非硬性变形。 具体地说, 鉴于一个源网格和想要的目标控管位置, 描述部分表面变形, 我们预测了一个连续变形的变形场, 在 3D 空间 定义的3D 空间里, 我们引入一个基于变形变形的变形网络, 作为地方表面变形的构成。 它学习了一套基于 3D 空间的本地隐形代码, 我们可以从中学习一套基于本地表面的连续变形功能。 我们的方法可以应用到挑战性变形的变形和变形方法, 将我们最新的变形方法, 都用来验证我们的变形, 变形到变形到变形, 变形方法。