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

Hugging Face Daily Papers Papers
dense-correspondence computer-vision image-editing identity-preservation foundation-models metric-learning

Summary

论文提出 FreeMatching，一个结合生成式与语义基础表征、通过异构监督和教师引导迭代精化的通用密集对应匹配框架，在图像编辑与参考引导生成任务上显著提升对应质量，并可作为身份保持的量化评估指标。

Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and rigid geometry. While effective for classical tasks, these assumptions break down in image editing and reference-guided generation (IEG), where transformations can preserve visual identity while breaking physical continuity. To establish identity-preserving correspondence across such transformations, we introduce FreeMatching, a generalizable framework combining generative and semantic foundation representations with heterogeneous supervision from classical datasets, tracked videos, and synthetic scenes. Teacher-guided iterative refinement further improves correspondence in IEG without dense correspondence annotations. Experimentally, a single FreeMatching model substantially improves correspondence quality on challenging IEG image pairs while retaining competitive performance on classical benchmarks. Furthermore, we demonstrate its utility as a quantitative metric for evaluating identity preservation, with scores that correlate with human judgment. The code is available at https://github.com/luping-liu/FreeMatching.
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Paper page - Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching

Source: https://huggingface.co/papers/2610.12421

Abstract

Densecorrespondencematchinghashistoricallybeenboundedbysimplifyingspatio-temporalpriors,suchassmoothmotionandrigidgeometry.Whileeffectiveforclassicaltasks,theseassumptionsbreakdowninimageeditingandreference-guidedgeneration(IEG),wheretransformationscanpreservevisualidentitywhilebreakingphysicalcontinuity.Toestablishidentity-preservingcorrespondenceacrosssuchtransformations,weintroduceFreeMatching,ageneralizableframeworkcombininggenerativeandsemanticfoundationrepresentationswithheterogeneoussupervisionfromclassicaldatasets,trackedvideos,andsyntheticscenes.Teacher-guidediterativerefinementfurtherimprovescorrespondenceinIEGwithoutdensecorrespondenceannotations.Experimentally,asingleFreeMatchingmodelsubstantiallyimprovescorrespondencequalityonchallengingIEGimagepairswhileretainingcompetitiveperformanceonclassicalbenchmarks.Furthermore,wedemonstrateitsutilityasaquantitativemetricforevaluatingidentitypreservation,withscoresthatcorrelatewithhumanjudgment.Thecodeisavailableathttps://github.com/luping-liu/FreeMatching.

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