Person re-identification via 3D skeletons is an important emerging research area that attracts increasing attention within the pattern recognition community. With distinctive advantages across various application scenarios, numerous 3D skeleton based person re-identification (SRID) methods with diverse skeleton modeling and learning paradigms have been proposed in recent years. In this paper, we provide a comprehensive review and analysis of recent SRID advances. First of all, we define the SRID task and provide an overview of its origin and major advancements. Secondly, we formulate a systematic taxonomy that organizes existing methods into three categories centered on hand-crafted, sequence-based, and graph-based modeling. Then, we elaborate on the representative models along these three types with an illustration of foundational mechanisms. Meanwhile, we provide an overview of mainstream supervised, self-supervised, and unsupervised SRID learning paradigms and corresponding common methods. A thorough evaluation of state-of-the-art SRID methods is further conducted over various types of benchmarks and protocols to compare their effectiveness, efficiency, and key properties. Finally, we present the key challenges and prospects to advance future research, and highlight interdisciplinary applications of SRID with a case study.

翻译：基于3D骨架的行人重识别是模式识别领域备受关注的重要新兴研究方向。凭借在不同应用场景中的显著优势，近年来涌现出大量基于3D骨架的行人重识别方法，涵盖了多样化的骨架建模与学习范式。本文对近期SRID研究进展进行了全面回顾与分析。首先，我们界定了SRID任务并概述其起源与主要进展。其次，我们建立了系统化的分类体系，将现有方法归纳为基于手工特征、序列建模和图结构建模三大类别。随后，我们沿此分类详细阐述了各类代表性模型，并图解其基础机制。同时，我们系统梳理了主流的监督式、自监督式与无监督式SRID学习范式及其对应典型方法。通过对各类基准测试与评估协议的全面评估，进一步比较了前沿SRID方法的有效性、效率与关键特性。最后，我们提出了推动未来研究的关键挑战与前景展望，并通过案例研究重点探讨了SRID在跨学科领域的应用潜力。