基于3D姿态的花样滑冰时序动作分割：一种细粒度且跳跃过程感知的标注方法
3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach
AI总结：
针对花样滑冰缺乏3D姿态TAS数据集和方法的问题，本文提出了FS-Jump3D数据集及细粒度跳跃过程感知标注方法，验证了3D姿态特征与细粒度数据集在TAS模型中的有效性。
摘要：
从视频中理解人类动作在包括体育在内的许多领域都至关重要。在花样滑冰中，技术评判是通过观察滑冰者的3D动作来进行的，其评判过程的一部分可以被视为时序动作分割（TAS）任务。在花样滑冰中，自动为视频分配时序语义的TAS任务正被积极研究。然而，对于需要3D姿态数据的TAS任务，目前缺乏相应的数据集和有效方法。在本研究中，我们首先利用光学无标记动作捕捉技术，创建了包含复杂且动态的花样滑冰跳跃的FS-Jump3D数据集。我们还提出了一种新的细粒度花样滑冰跳跃TAS数据集标注方法，使TAS模型能够学习跳跃过程。在实验结果中，我们验证了3D姿态特征作为输入以及细粒度数据集在花样滑冰TAS模型中的有效性。FS-Jump3D数据集可在https://github.com/ryota-skating/FS-Jump3D获取。
英文摘要：
Understanding human actions from videos is essential in many domains, including sports. In figure skating, technical judgments are performed by watching skaters' 3D movements, and its part of the judging procedure can be regarded as a Temporal Action Segmentation (TAS) task. TAS tasks in figure skating that automatically assign temporal semantics to video are actively researched. However, there is a lack of datasets and effective methods for TAS tasks requiring 3D pose data. In this study, we first created the FS-Jump3D dataset of complex and dynamic figure skating jumps using optical markerless motion capture. We also propose a new fine-grained figure skating jump TAS dataset annotation method with which TAS models can learn jump procedures. In the experimental results, we validated the usefulness of 3D pose features as input and the fine-grained dataset for the TAS model in figure skating. FS-Jump3D Dataset is available at https://github.com/ryota-skating/FS-Jump3D.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
