In the challenging realm of image-to-image translation, most traditional methods require separate models for different translation directions, leading to inefficient use of computational resources. This paper introduces the Bidirectional Brownian Bridge Diffusion Model (BiBBDM), a novel approach that leverages Brownian Bridge processes for bidirectional image-to-image translation. Unlike conventional Diffusion Models (DMs) that treat image-to-image translation as a unidirectional conditional generation process, BiBBDM models the translation as a stochastic Brownian Bridge process, enabling simultaneous learning of bidirectional translation between two domains. This innovation allows our method to achieve bidirectional image translation using different sampling directions of a single model, eliminating the need for multiple models for both translation directions. To the best of our knowledge, BiBBDM is the first image translation framework to achieve simultaneous dualdomain sampling with the same model and parameters, based on Brownian Bridge diffusion processes. Extensive experimental results on various benchmarks demonstrate that BiBBDM achieves competitive performance, as evidenced by both visual inspection and quantitative metrics.
Xue et al. (Mon,) studied this question.