Unsupervised Disentangling Structure and Appearance

Wayne Wu, Kaidi Cao, Cheng Li, Chen Qian, Chen Change Loy

Abstract: It is challenging to disentangle an object into two orthogonal spaces of structure and appearance since each can influence the visual observation in a different and unpredictable way. It is rare for one to have access to a large number of data to help separate the influences. In this paper, we present a novel framework to learn this disentangled representation in a completely unsupervised manner. We address this problem in a two-branch Variational Autoencoder framework. For the structure branch, we project the latent factor into a soft structured point tensor and constrain it with losses derived from prior knowledge. This encourages the branch to distill geometry information. Another branch learns the complementary appearance information. The two branches form an effective framework that can disentangle object's structure-appearance representation without any human annotation. We evaluate our approach on four image datasets, on which we demonstrate the superior disentanglement and visual analogy quality both in synthesis and real-world data. We are able to generate photo-realistic images with 256*256 resolution that are clearly disentangled in structure and appearance.

Keywords: disentangled representations, VAE, generative models, unsupervised learning

TL;DR: We present a novel framework to learn the disentangled representation of structure and appearance in a completely unsupervised manner.