Shape deformation is an important component in any geometry processing toolbox. The goal is to enable intuitive deformations of single or multiple shapes or to transfer example deformations to new shapes while preserving the plausibility of the deformed shape(s). Existing approaches assume access to point-level or part-level correspondence or establish them in a preprocessing phase, thus limiting the scope and generality of such approaches. We propose DeformSyncNet, a new approach that allows consistent and synchronized shape deformations without requiring explicit correspondence information. Technically, we achieve this by encoding deformations into a class-specific idealized latent space while decoding them into an individual, model-specific linear deformation action space, operating directly in 3D. The underlying encoding and decoding are performed by specialized (jointly trained) neural networks. By design, the inductive bias of our networks results in a deformation space with several desirable properties, such as path invariance across different deformation pathways, which are then also approximately preserved in real space. We qualitatively and quantitatively evaluate our framework against multiple alternative approaches and demonstrate improved performance.

中文翻译：

---

DeformSyncNet：通过同步形状变形空间进行变形传递

形状变形是任何几何处理工具箱中的重要组成部分。目标是实现单个或多个形状的直观变形，或将示例变形转换为新形状，同时保持变形形状的合理性。现有方法假设可以访问点级别或部分级别的对应关系或在预处理阶段建立它们，从而限制了此类方法的范围和通用性。我们提出了 DeformSyncNet，这是一种新方法，它允许一致和同步的形状变形，而无需明确的对应信息。从技术上讲，我们通过将变形编码到特定于类的理想化潜在空间中，同时将它们解码为单独的、特定于模型的线性变形动作空间，直接在 3D 中操作来实现这一点。底层编码和解码由专门的（联合训练的）神经网络执行。通过设计，我们网络的归纳偏置导致变形空间具有几个理想的属性，例如跨不同变形路径的​​路径不变性，然后这些属性也大致保留在真实空间中。我们针对多种替代方法对我们的框架进行了定性和定量评估，并展示了改进的性能。