Clothes-changing person re-identification (CC-ReID) aims to recognize individuals despite drastic appearance changes caused by clothing variation. While existing methods rely on adversarial learning to disentangle clothing features, we propose Ortho-ReID, which explicitly models a low-rank clothing subspace from VLM text descriptions and extracts clothing-invariant representations via direct geometric constraints. A critical component is our transformer-based Basis Maker, which refines a shared, low-dimensional clothing prior into an instance-adaptive low-rank subspace through cross-attention with image patches, enabling robust clothing feature extraction even under varying visibility conditions. This instance-adaptive subspace is supervised via alignment with clothing text embeddings, while identity features are extracted via a learnable projection head and geometrically constrained to be strictly orthogonal to it. Extensive experiments demonstrate state-of-the-art performance on PRCC (+5.9% top-1), Celeb-reID-light (+3.5%), and LaST (+5.3%), with competitive results on LTCC.

翻译：换衣行人重识别（CC-ReID）旨在应对因衣物变化引起的剧烈外观差异，实现个体身份识别。现有方法依赖对抗学习以解耦服装特征，本文提出 Ortho-ReID 方法，通过显式地从 VLM 文本描述中建模低秩衣物子空间，并利用直接几何约束提取衣物不变表征。其核心组件是基于 Transformer 的 Basis Maker，该模块通过跨注意力机制与图像补丁交互，将共享的低维衣物先验精炼为实例自适应的低秩子空间，从而在可见条件变化下仍能稳健提取衣物特征。该实例自适应子空间通过与衣物文本嵌入的对齐进行监督，而身份特征则通过可学习的投影头提取，并受到严格正交的几何约束。大量实验表明，本方法在 PRCC（top-1 提升 5.9%）、Celeb-reID-light（+3.5%）和 LaST（+5.3%）数据集上达到最优性能，并在 LTCC 数据集上取得具有竞争力的结果。