AI终于能认出“同一个人”了
以前AI找两张图里的对应点，靠的是“东西不会乱动”的假设——适合视频里追踪物体，但一遇到图像编辑就抓瞎：你把一个人的脸P到另一个姿势上，物理位置全变了，AI就认不出这是同一个人。这篇论文换了个思路：不再依赖位置，而是靠“这个人的长相特征”来匹配，把生成模型和语义模型的特征拼在一起，再用传统数据、视频和合成场景混合训练。结果是在图像编辑这种“大变活人”的场景里，匹配准确率大幅提升，而且它还能当一把尺子，量“AI有没有保住这个人原本的样子”，打分跟人眼判断高度一致。它不是你明天就能用的工具，但这是AI从“看位置”进化到“看本质”的一步。
📄 原文摘要(英文)
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.