Towards Purely Unsupervised Disentanglement of Appearance and Shape for Person Images Generation

TL;DR

This paper introduces an unsupervised method for disentangling appearance and shape in person images, enabling high-quality image synthesis without annotations or external clues.

Contribution

It proposes a novel unsupervised framework that learns shape and appearance features simultaneously, using adversarial, color consistency, and reconstruction losses, without relying on annotations or external cues.

Findings

Achieves effective disentanglement of appearance and shape.

Produces high-quality novel image synthesis.

Outperforms some weakly-supervised methods in experiments.

Abstract

There have been a fairly of research interests in exploring the disentanglement of appearance and shape from human images. Most existing endeavours pursuit this goal by either using training images with annotations or regulating the training process with external clues such as human skeleton, body segmentation or cloth patches etc. In this paper, we aim to address this challenge in a more unsupervised manner---we do not require any annotation nor any external task-specific clues. To this end, we formulate an encoder-decoder-like network to extract both the shape and appearance features from input images at the same time, and train the parameters by three losses: feature adversarial loss, color consistency loss and reconstruction loss. The feature adversarial loss mainly impose little to none mutual information between the extracted shape and appearance features, while the color consistency loss is to encourage the invariance of person appearance conditioned on different shapes. More importantly, our unsupervised (Unsupervised learning has many interpretations in different tasks. To be clear, in this paper, we refer unsupervised learning as learning without task-specific human annotations, pairs or any form of weak supervision.) framework utilizes learned shape features as masks which are applied to the input itself in order to obtain clean appearance features. Without using fixed input human skeleton, our network better preserves the conditional human posture while requiring less supervision. Experimental results on DeepFashion and Market1501 demonstrate that the proposed method achieves clean disentanglement and is able to synthesis novel images of comparable quality with state-of-the-art weakly-supervised or even supervised methods.