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SapiensID: Foundation for Human Recognition
Conference proceeding

SapiensID: Foundation for Human Recognition

Minchul Kim, Dingqiang Ye, Yiyang Su, Feng Liu and Xiaoming Liu
Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online), pp 13937-13947
10 Jun 2025

Abstract

Adaptation models Benchmark testing body recognition body reid Face recognition Limiting patchify recognition Retina Tokenization Training vision transformer Ethics Intellectual Property Semantics
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. Project Link

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
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