Li, Bo, Xue, Kaitao, Lui, Bin and Lai, Yukun  ORCID: https://orcid.org/0000-0002-2094-5680
2023.
BBDM: Image-to-image translation with Brownian bridge diffusion models.
Presented at: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
Vancouver, Canada,
18-22 June 2023.
Proceedings of Conference on Computer Vision and Pattern Recognition.
, vol.2023
IEEE,
pp. 1952-1961.
10.1109/CVPR52729.2023.00194
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Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | IEEE |
| ISBN: | 9798350301304 |
| ISSN: | 1063-6919 |
| Date of First Compliant Deposit: | 20 April 2023 |
| Date of Acceptance: | 27 February 2023 |
| Last Modified: | 12 Dec 2023 13:22 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/158968 |
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