We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least approximately, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences.

中文翻译：

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从野外图像中无监督学习可能对称的可变形3D对象。

我们提出了一种无需原始监督即可从原始单视图图像中学习3D变形对象类别的方法。该方法基于自动编码器，该自动编码器将每个输入图像分解为深度，反照率，视点和照明。为了在不进行监督的情况下解开这些组件，我们使用以下事实：许多对象类别至少具有近似对称的结构。我们证明了关于照明的推理使我们能够利用基本的对象对称性，即使由于阴影而导致外观不对称。此外，我们通过预测对称概率图来建模可能（但不一定）对称的对象，并与模型的其他组件进行端到端学习。我们的实验表明，该方法可以非常准确地恢复人脸的3D形状，单视场图像中的猫脸和汽车，无需任何监督或事先的造型模型。在基准测试中，与另一种在2D图像对应级别上使用监督的方法相比，我们证明了更高的准确性。