We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation. A new deformation can then be generated by sampling the coefficients of the meta-handles in a specific range. We employ biharmonic coordinates as the deformation function, which can smoothly propagate the control points' translations to the entire mesh. To avoid learning zero deformation as meta-handles, we incorporate a target-fitting module which deforms the input mesh to match a random target. To enhance deformations' plausibility, we employ a soft-rasterizer-based discriminator that projects the meshes to a 2D space. Our experiments demonstrate the superiority of the generated deformations as well as the interpretability and consistency of the learned meta-handles.

中文翻译：

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DeepMetaHandles：使用双谐坐标学习3D网格的变形元句柄

我们提出了DeepMetaHandles，这是一个基于网格变形的3D条件生成模型。给定一个类别的3D网格及其变形手柄（控制点）的集合，我们的方法将为每种形状学习一组元手柄，这些元手柄表示为给定手柄的组合。解开的元手柄会分解形状的所有可能的变形，而每个变形都对应一个直观的变形。然后可以通过在特定范围内采样元句柄的系数来生成新的变形。我们采用双谐波坐标作为变形函数，可以将控制点的平移平稳地传播到整个网格。为了避免学习零变形作为元句柄，我们合并了一个目标拟合模块，该模块可使输入网格变形以匹配随机目标。为了增强变形的可信度，我们使用了基于软光栅的鉴别器，将网格投影到2D空间。我们的实验证明了所生成变形的优越性以及所学元句柄的可解释性和一致性。