Prevalent use of motion capture (MoCap) produces large volumes of data and MoCap data retrieval becomes crucial for efficient data reuse. MoCap clips may not be neatly segmented and labeled, increasing the difficulty of retrieval. In order to effectively retrieve such data, we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment (PESTA) in this work. It retrieves similarities at the sub-sequence level, achieves robustness against singular frames and enables control of tradeoff between precision and efficiency. It firstly learns a dictionary of encoded postures utilizing unsupervised adversarial autoencoder techniques and, based on which, compactly symbolizes any MoCap sequence. Secondly, it conducts strided temporal alignment to align a query sequence to repository sequences to retrieve the best-matching sub-sequences from the repository. Further, it extends to find matches for multiple sub-queries in a long query at sharply promoted efficiency and minutely sacrificed precision. Outstanding performance of the proposed scheme is well demonstrated by experiments on two public MoCap datasets and one MoCap dataset captured by ourselves.

动作捕捉 (MoCap) 的普遍使用会产生大量数据，而 MoCap 数据检索对于高效数据重用至关重要。动捕剪辑可能无法整齐地分割和标记，从而增加了检索的难度。为了有效地检索此类数据，我们在这项工作中提出了一种通过无监督姿势编码和跨步时间对齐（PESTA）的基于弹性内容的检索方案。它检索子序列级别的相似性，实现针对奇异帧的鲁棒性，并能够控制精度和效率之间的权衡。它首先利用无监督的对抗性自动编码器技术学习编码姿势字典，并在此基础上紧凑地符号化任何 MoCap 序列。其次，它进行跨步时间对齐，将查询序列与存储库序列对齐，以从存储库中检索最佳匹配的子序列。此外，它还扩展到在长查询中查找多个子查询的匹配，从而大大提高了效率并略微牺牲了精度。在两个公共 MoCap 数据集和一个我们自己捕获的 MoCap 数据集上进行的实验很好地证明了所提出方案的出色性能。