BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models
This addresses the domain gap issue in image-to-image translation for computer vision applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of image-to-image translation by proposing a Brownian Bridge Diffusion Model (BBDM) that models translation as a stochastic Brownian bridge process, achieving competitive performance on various benchmarks through visual and measurable metrics.
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.