Abstract
Existing human recognition systems often rely on separate, specialized models for face and body analysis, limiting their effectiveness in real-world scenarios where pose, visibility, and context vary widely. This paper introduces SapiensID, a unified model that bridges this gap, achieving robust performance across diverse settings. SapiensID introduces (i) Retina Patch (RP), a dynamic patch generation scheme that adapts to subject scale and ensures consistent tokenization of regions of interest, (ii) a masked recognition model (MRM) that learns from variable token length, and (iii) Semantic Attention Head (SAH), an module that learns pose-invariant representations by pooling features around key body parts. To facilitate training, we introduce WebBody4M, a large-scale dataset capturing diverse poses and scale variations. Extensive experiments demonstrate that SapiensID achieves state-of-the-art results on various body ReID benchmarks, outperforming specialized models in both short-term and long-term scenarios while remaining competitive with dedicated face recognition systems. Furthermore, SapiensID establishes a strong baseline for the newly introduced challenge of Cross Pose-Scale ReID, demonstrating its ability to generalize to complex, real-world conditions.
Abstract (translated)
现有的人类识别系统通常依赖于专门针对面部和身体分析的独立模型，这限制了它们在姿势、可见性和上下文变化极大的现实场景中的有效性。本文介绍了SapiensID，这是一种统一的模型，它弥合了这一差距，在各种设置中实现了稳健的表现。 SapiensID引入了以下创新： (i) Retina Patch (RP)，一种动态补丁生成方案，可以根据主体尺度进行调整，并确保对感兴趣区域的一致标记化处理； (ii) 一个遮罩识别模型（Masked Recognition Model, MRM），可以从可变的令牌长度中学习； (iii) 语义注意力头（Semantic Attention Head, SAH），通过围绕关键身体部位汇集特征来学习与姿势无关的表示。 为了促进训练，我们引入了WebBody4M，这是一个大规模的数据集，捕捉各种姿态和尺度的变化。广泛的实验表明，SapiensID在多种人体重识别基准上达到了最先进的结果，在短期和长期场景中均优于专用模型，并且在专为人脸识别系统提供的方法中保持竞争力。 此外，SapiensID为新引入的跨姿势-尺度重识别挑战建立了强大的基线，展示了其在复杂、现实世界条件下的泛化能力。
URL
https://arxiv.org/abs/2504.04708
