A${}^2$BM: Alignment-Aware Bridge Matching for Image-to-Image Translation

2026-07-13

AI Summary

This work addresses the challenge of weakly aligned training data in image-to-image translation, which arises from discrepancies in acquisition conditions such as asynchronous capture, illumination variations, or registration errors. The authors propose an alignment-aware bridge matching method that explicitly models alignment information as a conditional variable within a bridge matching framework. By introducing an alignment score to distinguish genuine semantic correspondences from misalignment artifacts, the method enables fidelity-controllable translation during inference. Built upon a unified framework bridging bridge matching and flow matching, the approach effectively leverages weakly aligned data and significantly outperforms existing baselines—including GANs, diffusion models, and Schrödinger bridge methods—on tasks such as cross-sensor super-resolution and unsupervised domain adaptation.

Abstract

Paired image-to-image translation underpins a wide range of computer vision tasks, including image editing, sensor translation, and domain adaptation. Bridge matching and flow matching have recently emerged as powerful frameworks, extending diffusion models to arbitrary source and target distributions. However, their standard formulations assume perfectly aligned training pairs, treating all source-target correspondences as equally reliable. In practice, real-world applications often involve weakly aligned pairs due to changes of acquisition conditions, including e.g. asynchronous captures, different illuminations, or misregistration. In this work, we introduce Alignment-Aware Bridge Matching (A${}^2$BM), a bridge matching method that leverages image pairs alignment during training. By incorporating alignment scores, the model learns to disentangle true semantic correspondences from misalignment artifacts. At inference time, we use the alignment score as a control variable over translation fidelity, with strongly aligned outputs obtained when prompting the model with the highest alignment score. We validate A${}^2$BM on both controlled synthetic experiments and on challenging real-world tasks, including cross-sensor super-resolution and pixel-space unsupervised domain adaptation. In all settings, A${}^2$BM consistently improves translation fidelity over strong GAN-, diffusion-, and Schr{ö}dinger bridge-based baselines, establishing alignment conditioning as a principled solution for image translation models with weakly aligned data.

Problem

• image-to-image translation
• weakly aligned pairs
• alignment artifacts
• translation fidelity
• domain adaptation

Innovation

• Alignment-Aware Bridge Matching
• image-to-image translation
• weakly aligned pairs
• bridge matching
• alignment conditioning