通过2D-3D对应关系探索换衣行人重识别的形状嵌入
Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences
AI总结：
本文提出连续表面对应学习（CSCL），通过像素到顶点分类建立2D图像与3D人体表面的对应关系以学习形状嵌入，并构建首个大规模换衣重识别数据集DP3D，有效提升了换衣行人重识别性能。
摘要：
换衣行人重识别（CC-ReID）是一个常见且现实的问题，因为时尚随时间不断变化，人们的审美偏好也并非一成不变。虽然现有的大多数换衣重识别方法侧重于从粗粒度语义线索（如剪影和部位分割图）中学习与衣物无关的身份表示，但它们忽略了像素级别上连续的形状分布。在本文中，我们提出了连续表面对应学习（CSCL），这是一种用于换衣重识别的新形状嵌入范式。CSCL通过像素到顶点分类在2D图像平面与规范3D人体表面之间建立连续对应关系，从而自然地将人物图像对齐到3D人体模型表面，并同时获得像素级的表面嵌入。我们进一步从学习到的表面嵌入中提取细粒度形状特征，然后通过精心设计的跨模态融合模块将其与全局RGB特征进行整合。基于2D-3D对应关系的形状嵌入范式显著增强了模型对人体形状的全局理解能力。为促进换衣场景下重识别的研究，我们构建了3D Dense Persons（DP3D），这是第一个大规模换衣重识别数据集，为每张人物图像提供密集标注的2D-3D对应关系和精确的3D网格，同时包含覆盖四季的多样化换衣案例。在换衣和衣物一致的重识别基准上的实验验证了我们方法的有效性。
英文摘要：
Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
