Separating Content and Style for Unsupervised Image-to-Image Translation

TL;DR

This paper introduces a unified framework for unsupervised image-to-image translation that separates content and style codes simultaneously, enhancing interpretability, manipulation, and translation quality.

Contribution

It proposes a novel method to disentangle content and style codes together, improving multimodal translation and interpretability over prior approaches.

Findings

Outperforms existing methods in visual quality

Achieves higher diversity in translated images

Enhances interpretability and manipulation capabilities

Abstract

Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples. Existing works focus on disentangling domain-invariant content code and domain-specific style code individually for multimodal purposes. However, less attention has been paid to interpreting and manipulating the translated image. In this paper, we propose to separate the content code and style code simultaneously in a unified framework. Based on the correlation between the latent features and the high-level domain-invariant tasks, the proposed framework demonstrates superior performance in multimodal translation, interpretability and manipulation of the translated image. Experimental results show that the proposed approach outperforms the existing unsupervised image translation methods in terms of visual quality and diversity.