Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation

TL;DR

This paper proposes a novel method to unify label- and reference-based image-to-image translation by embedding both into a shared space, enabling mutual enhancement and diverse high-quality multi-attribute translations.

Contribution

It introduces encoding modules that compare domain differences and a cycle consistency mechanism to bridge label- and reference-based synthesis in multi-attribute I2IT.

Findings

Mutually promotes label- and reference-based synthesis.

Achieves diverse results with high quality and style consistency.

Demonstrates improved translation performance through shared embedding.

Abstract

The image-to-image translation (I2IT) model takes a target label or a reference image as the input, and changes a source into the specified target domain. The two types of synthesis, either label- or reference-based, have substantial differences. Particularly, the label-based synthesis reflects the common characteristics of the target domain, and the reference-based shows the specific style similar to the reference. This paper intends to bridge the gap between them in the task of multi-attribute I2IT. We design the label- and reference-based encoding modules (LEM and REM) to compare the domain differences. They first transfer the source image and target label (or reference) into a common embedding space, by providing the opposite directions through the attribute difference vector. Then the two embeddings are simply fused together to form the latent code S_rand (or S_ref), reflecting the domain style differences, which is injected into each layer of the generator by SPADE. To link LEM and REM, so that two types of results benefit each other, we encourage the two latent codes to be close, and set up the cycle consistency between the forward and backward translations on them. Moreover, the interpolation between the S_rand and S_ref is also used to synthesize an extra image. Experiments show that label- and reference-based synthesis are indeed mutually promoted, so that we can have the diverse results from LEM, and high quality results with the similar style of the reference.