---
格式版本: 2
标题: "SapiensID 2.0: Aligning Human Recognition Foundation Models with Human Perception"
原文链接: "https://arxiv.org/abs/2608.10497"
发布日期: "2026-08-11"
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AI优质: "否"
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去重键: "https://arxiv.org/abs/2608.10497"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:SapiensID 2.0: Aligning Human Recognition Foundation Models with Human Perception

Authors:[Yiyang Su](https://arxiv.org/search/cs?searchtype=author&query=Su,+Y), [Jie Zhu](https://arxiv.org/search/cs?searchtype=author&query=Zhu,+J), [Feng Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+F), [Anil K. Jain](https://arxiv.org/search/cs?searchtype=author&query=Jain,+A+K), [Xiaoming Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+X)

[View PDF](https://arxiv.org/pdf/2608.10497)

> Abstract:While foundation models have significantly advanced human recognition across diverse modalities, they predominantly rely on static, geometric feature extraction. This approach fundamentally diverges from human perception. Consequently, current models often suffer from "semantic blindness," overfitting to transient noise while failing to leverage invariant soft biometrics, and struggle to capture temporal motion signatures. To bridge this gap, we propose SapiensID 2.0, a human recognition framework enriched with both semantic and temporal awareness. To overcome the lack of soft-biometric annotations, we transfer zero-shot semantic knowledge from Multimodal Large Language Models (MLLMs) into a discriminative embedding space. We resolve the dimensional mismatch between these spaces using Invariant Trait Alignment (ITA) to distill core persistent traits, and Transient Noise Disentanglement (TND) to decouple artifacts like clothing. Furthermore, we design a Kinematic Semantic Attention Head (K-SAH) that extends spatial attention across temporal windows. By tracking semantic patches over time, K-SAH captures rich kinematic signatures without requiring large-scale video datasets. Extensive experiments demonstrate that SapiensID 2.0 achieves state-of-the-art performance across image- and video-based person re-identification and gait recognition, while maintaining robust face recognition capabilities.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| --- | --- |
| Cite as: | [arXiv:2608.10497](https://arxiv.org/abs/2608.10497) \[cs.CV\] |
|  | (or [arXiv:2608.10497v1](https://arxiv.org/abs/2608.10497v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10497](https://doi.org/10.48550/arXiv.2608.10497) |

## Submission history

From: Yiyang Su \[[view email](https://arxiv.org/show-email/ca5eb43b/2608.10497)\]  
**\[v1\]** Tue, 11 Aug 2026 05:13:05 UTC (2,307 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10497) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
