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

Paper recorded by Signals 4 on 2026-10-08 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09

Category: cs.LG · 机器学习 · first seen 2026-10-09

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 ac

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