Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators
Bohan Xiao, Peiyong Wang, Qisheng He, Ming Dong
Abstract
Image-to-Image (I2I) translation involves converting an im- age from one domain to another. Deterministic I2I transla- tion, such as in image super-resolution, extends this con- cept by guaranteeing that each input generates a consistent and predictable output, closely matching the ground truth (GT) with high fidelity. In this paper, we propose a denois- ing Brownian bridge model with dual approximators (Dual- approx Bridge), a novel generative model that exploits the Brownian bridge dynamics and two neural network-based approximators (one for forward and one for reverse pro- cess) to produce faithful output with negligible variance and high image quality in I2I translations. Our extensive exper- iments on benchmark datasets including image generation and super-resolution demonstrate the consistent and supe- rior performance of Dual-approx Bridge in terms of im- age quality and faithfulness to GT when compared to both stochastic and deterministic baselines. Project page and code: https://github.com/bohan95/dual-app-bridge
BibTeX
@InProceedings{Xiao_2025_CVPR,
author = {Xiao, Bohan and Wang, Peiyong and He, Qisheng and Dong, Ming},
title = {Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28232-28241}
}