We propose a novel learnable representation for detail-preserving shape deformation. The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source. Our method extends a traditional cage-based deformation technique, where the source shape is enclosed by a coarse control mesh termed \emph{cage}, and translations prescribed on the cage vertices are interpolated to any point on the source mesh via special weight functions. The use of this sparse cage scaffolding enables preserving surface details regardless of the shape's intricacy and topology. Our key contribution is a novel neural network architecture for predicting deformations by controlling the cage. We incorporate a differentiable cage-based deformation module in our architecture, and train our network end-to-end. Our method can be trained with common collections of 3D models in an unsupervised fashion, without any cage-specific annotations. We demonstrate the utility of our method for synthesizing shape variations and deformation transfer.

中文翻译：

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用于保留 3D 变形细节的神经笼

我们为保留细节的形状变形提出了一种新颖的可学习表示。我们方法的目标是扭曲源形状以匹配目标形状的一般结构，同时保留源的表面细节。我们的方法扩展了传统的基于笼子的变形技术，其中源形状被称为 \emph {cage} 的粗控制网格包围，并且笼子顶点上规定的平移通过特殊的权重函数插入到源网格上的任何点。无论形状的复杂程度和拓扑如何，使用这种稀疏笼式脚手架都可以保留表面细节。我们的主要贡献是一种新颖的神经网络架构，用于通过控制笼来预测变形。我们在我们的架构中加入了一个可微的基于笼子的变形模块，并端到端地训练我们的网络。我们的方法可以以无监督的方式使用常见的 3D 模型集合进行训练，无需任何特定于笼子的注释。我们展示了我们的方法在合成形状变化和变形传递方面的实用性。