Abstract
Gait recognition is an important biometric for human identification at a distance, particularly under low-resolution or unconstrained environments. Current works typically focus on either 2D representations (e.g., silhouettes and skeletons) or 3D representations (e.g., meshes and SMPLs), but relying on a single modality often fails to capture the full geometric and dynamic complexity of human walking patterns. In this paper, we propose a multi-modal and multi-task framework that combines 2D temporal silhouettes with 3D SMPL features for robust gait analysis. Beyond identification, we introduce a multitask learning strategy that jointly performs gait recognition and human attribute estimation, including age, body mass index (BMI), and gender. A unified transformer is employed to effectively fuse multi-modal gait features and better learn attribute-related representations, while preserving discriminative identity cues. Extensive experiments on the large-scale BRIAR datasets, collected under challenging conditions such as long-range distances (up to 1 km) and extreme pitch angles (up to 50°), demonstrate that our approach outperforms state-of-the-art methods in gait recognition and provides accurate human attribute estimation. These results highlight the promise of multi-modal and multitask learning for advancing gait-based human understanding in real-world scenarios.
Abstract (translated)
步态识别是一种重要的生物特征，用于在远距离下进行人体识别，尤其是在低分辨率或非约束环境中。当前的研究通常集中在二维表示（如轮廓和骨架）或三维表示（如网格和SMPL模型）上，但依赖单一模态往往无法捕捉到人类行走模式中的完整几何和动态复杂性。在这篇论文中，我们提出了一种多模态、多任务框架，该框架结合了2D时间轮廓与3D SMPL特征，以实现稳健的步态分析。除了识别功能之外，我们还引入了一种多任务学习策略，可以同时执行步态识别和人体属性估计（包括年龄、体质指数BMI以及性别）。一个统一的变压器被用来有效地融合多种模态下的步态特征，并更好地学习与属性相关的表示，同时保留了区分性的身份线索。在大规模BRIAR数据集上进行了广泛的实验，该数据集在具有挑战性条件下收集的数据，例如长距离（高达1公里）和极端俯仰角度（高达50°）。实验结果表明，我们的方法在步态识别方面超越了最先进的方法，并且能够提供准确的人体属性估计。这些成果突显了多模态与多任务学习对于推进基于步态的人类理解在现实世界场景中的应用前景。
URL
https://arxiv.org/abs/2510.10417
