CGCC: Towards Generalizable Clothes-Changing Person Re-Identification

Clothes-Changing Person Re-Identification (CC-ReID) aimsto match pedestrians across non-overlapping camera views despite vari-ations in clothes. However, current research is severely constrained bytwo main shortcomings: existing datasets lack comprehensive diversityand semantic annotations, and current methods fail to globally modeland eliminate identity-irrelevant interfering factors, severely limiting CC-ReID generalization in real-world scenarios. To address these limitations,we propose a unified framework for generalizable CC-ReID. First, weintroduce CGCC, a Comprehensive dataset for Generalizable CC-ReIDfeaturing extensive diversity and fine-grained semantic annotations, com-prising 4,101 identities and 217,248 images. Additionally, we introduceText-Guided Identity Refinement (TGIR), a novel framework that uti-lizes text descriptions to construct an identity-irrelevant subspace, andemploys Singular Value Decomposition (SVD) to holistically extract andeliminate environmental and clothes interference. Together, CGCC andTGIR form a mutually reinforcing closed loop, where the diverse dataand structured annotations of the former provide the essential founda-tion for the semantic disentanglement of the latter. Extensive exper-iments demonstrate that the comprehensive diversity of CGCC effec-tively improves model generalizability, and TGIR precisely extracts ro-bust and generalizable identity representations. Dataset is available athttps://github.com/zhi-time/CGCC.