| title | Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching |
|---|---|
| authors | Luping Liu, Bingyi Kang, Yifan Wang, Dong Xu |
| date | 2026-10-08 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.12421 |
| affiliation | The University of Hong Kong |
| upvotes | 13 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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. |
cite
@misc{liu2026beyond,
title={Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching},
author={Luping Liu and Bingyi Kang and Yifan Wang and Dong Xu},
year={2026},
eprint={2610.12421},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.12421}
}