We introduce KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D object to a target 3D object from the same object category. Our method analyzes the difference between the shapes of the two objects by comparing their latent representations. This latent representation is in the form of 3D keypoints that are learned in an unsupervised way. The difference between the 3D keypoints of the source and the target objects then informs the shape deformation algorithm that deforms the source object into the target object. The whole model is learned end-to-end and simultaneously discovers 3D keypoints while learning to use them for deforming object shapes. Our approach produces intuitive and semantically consistent control of shape deformations. Moreover, our discovered 3D keypoints are consistent across object category instances despite large shape variations. As our method is unsupervised, it can be readily deployed to new object categories without requiring annotations for 3D keypoints and deformations.

中文翻译：

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KeypointDeformer：用于形状控制的无监督3D关键点发现

我们介绍KeypointDeformer，这是一种通过自动发现的3D关键点进行形状控制的新型无监督方法。我们将此归结为将同一对象类别中的源3D对象与目标3D对象对齐的问题。我们的方法通过比较两个对象的潜在表示来分析两个对象的形状之间的差异。这种潜在表示形式是3D关键点的形式，这些关键点是在无人监督的情况下学习的。然后，源对象和目标对象的3D关键点之间的差异将通知形状变形算法，该算法将源对象变形为目标对象。整个模型是端到端学习的，同时发现3D关键点，同时学习使用3D关键点来变形对象形状。我们的方法可以对形状变形进行直观且语义一致的控制。此外，尽管形状变化很大，但我们发现的3D关键点在对象类别实例之间是一致的。由于我们的方法不受监督，因此可以轻松地部署到新的对象类别，而无需为3D关键点和变形添加注释。