  Image-to-image translation is an important and challenging problem in
computer vision and image processing. Diffusion models (DM) have shown great
potentials for high-quality image synthesis, and have gained competitive
performance on the task of image-to-image translation. However, most of the
existing diffusion models treat image-to-image translation as conditional
generation processes, and suffer heavily from the gap between distinct domains.
In this paper, a novel image-to-image translation method based on the Brownian
Bridge Diffusion Model (BBDM) is proposed, which models image-to-image
translation as a stochastic Brownian bridge process, and learns the translation
between two domains directly through the bidirectional diffusion process rather
than a conditional generation process. To the best of our knowledge, it is the
first work that proposes Brownian Bridge diffusion process for image-to-image
translation. Experimental results on various benchmarks demonstrate that the
proposed BBDM model achieves competitive performance through both visual
inspection and measurable metrics.
