Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching

The paper introduces FreeMatching, a framework that moves beyond traditional spatio-temporal priors for dense correspondence matching. It combines generative and semantic representations with diverse supervision sources, and uses teacher‑guided refinement to improve identity‑preserving correspondence in image editing and reference‑guided generation tasks. Experiments show that a single FreeMatching model outperforms existing methods on challenging image pairs while maintaining strong performance on classical benchmarks, and its scores correlate with human judgment of identity preservation.

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