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骨架的人物再识别是模式识别领域中一个重要的新兴研究方向，正日益受到关注。凭借其在多种应用场景中的独特优势，近年来涌现了大量基于不同骨架建模与学习范式的3D骨架人物再识别（SRID）方法。本文对近期SRID研究进展进行了全面综述与分析。首先，我们明确了SRID任务的定义，并概述其起源与主要发展历程。其次，我们构建了一个系统性的分类体系，将现有方法划分为基于手工特征、基于序列建模和基于图建模三大类。随后，我们详细阐述了这三类中的代表性模型及其基础机制。同时，我们对主流的监督式、自监督式和无监督式SRID学习范式及相应的通用方法进行了概述。此外，本文通过多种基准测试与评估协议对现有最优SRID方法进行了全面评估，以比较其有效性、效率及关键特性。最后，我们指出了推动未来研究的关键挑战与前景，并通过案例研究强调了SRID的跨学科应用价值。