Dense correspondence matching has historically been limited by simplifying spatio-temporal priors. The authors propose FreeMatching, a generalizable approach combining generative and semantic foundation representations with heterogeneous supervision from classical datasets, tracked videos, and synthetic scenes. Experiments show improved correspondence quality on challenging IEG image pairs.
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FreeMatching can be useful for tasks where preserving visual identity during complex image transformations, such as image editing and reference-guided generation, is important.
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