# Image Disentanglement and Uncooperative Re-Entanglement for High-Fidelity Image-to-Image Translation

## TL;DR

This paper introduces a novel high-fidelity image-to-image translation method that prevents network cooperation to preserve details and achieve more accurate, semantics-preserving translations, addressing limitations of existing cycle-consistency approaches.

## Contribution

The authors propose a new training technique that trains networks only on real data to improve fidelity and disentanglement in image translation, avoiding cooperation that leads to detail loss.

## Key findings

- Achieves more accurate, high-fidelity translations
- Effectively disentangles domain-specific factors
- Outperforms prior cycle-consistency methods

## Abstract

Cross-domain image-to-image translation should satisfy two requirements: (1) preserve the information that is common to both domains, and (2) generate convincing images covering variations that appear in the target domain. This is challenging, especially when there are no example translations available as supervision. Adversarial cycle consistency was recently proposed as a solution, with beautiful and creative results, yielding much follow-up work. However, augmented reality applications cannot readily use such techniques to provide users with compelling translations of real scenes, because the translations do not have high-fidelity constraints. In other words, current models are liable to change details that should be preserved: while re-texturing a face, they may alter the face's expression in an unpredictable way. In this paper, we introduce the problem of high-fidelity image-to-image translation, and present a method for solving it. Our main insight is that low-fidelity translations typically escape a cycle-consistency penalty, because the back-translator learns to compensate for the forward-translator's errors. We therefore introduce an optimization technique that prevents the networks from cooperating: simply train each network only when its input data is real. Prior works, in comparison, train each network with a mix of real and generated data. Experimental results show that our method accurately disentangles the factors that separate the domains, and converges to semantics-preserving translations that prior methods miss.