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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

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

Cl\'ement Chadebec , Onur Tasar , Sanjeev Sreetharan , Benjamin Aubin This is my paper
classification cs.CV
keywords bridgeimage-to-imagelatentmatchingmethodtaskstranslationdemonstrate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 7 Pith papers

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