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LBM: Latent Bridge Matching for Fast Image-to-Image Translation

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arxiv 2503.07535 v2 pith:W4LTRMO4 submitted 2025-03-10 cs.CV

Clément Chadebec , Onur Tasar , Sanjeev Sreetharan , Benjamin Aubin This is my paper · ORCID
classification cs.CV
keywords bridgeimage-to-imagelatentmatchingmethodtaskstranslationdemonstrate
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In this paper, we introduce Latent Bridge Matching (LBM), a new, versatile and scalable method that relies on Bridge Matching in a latent space to achieve fast image-to-image translation. We show that the method can reach state-of-the-art results for various image-to-image tasks using only a single inference step. In addition to its efficiency, we also demonstrate the versatility of the method across different image translation tasks such as object removal, normal and depth estimation, and object relighting. We also derive a conditional framework of LBM and demonstrate its effectiveness by tackling the tasks of controllable image relighting and shadow generation. We provide an implementation at https://github.com/gojasper/LBM.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Conditional flow matching with a rollout loss and early audio-visual fusion achieves state-of-the-art results on visually-guided acoustic highlighting.

  3. Spectral Consistent Flow for One-step 3D Medical Image Translation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A one-step latent Brownian-bridge flow plus frequency-domain gain correction produces more accurate 3D medical image translations than multi-step diffusion and prior single-step baselines across four datasets.

  4. Benchmarking GANs, Diffusion Models, and Flow Matching for T1w-to-T2w MRI Translation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    The GAN-based Pix2Pix model outperformed diffusion and flow matching models in a standardized T1w-to-T2w brain MRI translation benchmark on three datasets.

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