Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge

• URL: http://arxiv.org/abs/2602.16664v1
• Date: Wed, 18 Feb 2026 18:05:00 GMT
• Title: Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge
• Authors: Jiaming Liu, Felix Petersen, Yunhe Gao, Yabin Zhang, Hyojin Kim, Akshay S. Chaudhari, Yu Sun, Stefano Ermon, Sergios Gatidis,
• Abstract summary: Adversarial diffusion and diffusion-inversion methods have advanced unpaired image-to-image translation, but each faces key limitations.<n>We propose the Self-Supervised Semantic Bridge ( SSB), a versatile framework that integrates external semantic priors into diffusion bridge models.<n>Our key idea is to leverage self-supervised visual encoders to learn representations that are invariant to appearance changes but capture geometric structure.
• Score: 59.247871132422006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adversarial diffusion and diffusion-inversion methods have advanced unpaired image-to-image translation, but each faces key limitations. Adversarial approaches require target-domain adversarial loss during training, which can limit generalization to unseen data, while diffusion-inversion methods often produce low-fidelity translations due to imperfect inversion into noise-latent representations. In this work, we propose the Self-Supervised Semantic Bridge (SSB), a versatile framework that integrates external semantic priors into diffusion bridge models to enable spatially faithful translation without cross-domain supervision. Our key idea is to leverage self-supervised visual encoders to learn representations that are invariant to appearance changes but capture geometric structure, forming a shared latent space that conditions the diffusion bridges. Extensive experiments show that SSB outperforms strong prior methods for challenging medical image synthesis in both in-domain and out-of-domain settings, and extends easily to high-quality text-guided editing.

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