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Large intra-class variation is the result of changes in multiple objectcharacteristics. Images, however, only show the superposition of differentvariable factors such as appearance or shape. Therefore, learning todisentangle and represent these different characteristics poses a greatchallenge, especially in the unsupervised case. Moreover, large objectarticulation calls for a flexible part-based model. We present an unsupervisedapproach for disentangling appearance and shape by learning parts consistentlyover all instances of a category. Our model for learning an objectrepresentation is trained by simultaneously exploiting invariance andequivariance constraints between synthetically transformed images. Since nopart annotation or prior information on an object class is required, theapproach is applicable to arbitrary classes. We evaluate our approach on a widerange of object categories and diverse tasks including pose prediction,disentangled image synthesis, and video-to-video translation. The approachoutperforms the state-of-the-art on unsupervised keypoint prediction andcompares favorably even against supervised approaches on the task of shape andappearance transfer.

Unsupervised Part-Based Disentangling of Object Shape and Appearance