Markerless motion capture and understanding of professional non-daily human movements is an important yet unsolved task, which suffers from complex motion patterns and severe self-occlusion, especially for the monocular setting. In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input. Our approach utilizes the semantic and temporally structured sub-motion prior in the embedding space for motion capture and understanding in a data-driven multi-task manner. To enable robust capture under complex motion patterns, we propose an effective motion embedding module to recover both the implicit motion embedding and explicit 3D motion details via a corresponding mapping function as well as a sub-motion classifier. Based on such hybrid motion information, we introduce a multi-stream spatial-temporal Graph Convolutional Network(ST-GCN) to predict the fine-grained semantic action attributes, and adopt a semantic attribute mapping block to assemble various correlated action attributes into a high-level action label for the overall detailed understanding of the whole sequence, so as to enable various applications like action assessment or motion scoring. Comprehensive experiments on both public and our proposed datasets show that with a challenging monocular sports video input, our novel approach not only significantly improves the accuracy of 3D human motion capture, but also recovers accurate fine-grained semantic action attributes.
翻译:无标记的动作捕捉和理解专业非日常人类运动是一项重要但尚未解决的任务,它有复杂的运动模式和严重的自我封闭,特别是单体环境。在本文中,我们提议了SportCap -- -- 首次同时捕捉3D人类动议和理解单体具有挑战性体育视频输入的微细微粒动作的方法。我们的方法使用在嵌入空间之前的语义和时间结构小动作,以数据驱动的多任务方式进行运动捕捉和理解。为了能够在复杂的运动模式下进行强力捕捉,我们提议了一个有效的运动嵌入模块,通过相应的绘图功能和一个亚动分类器来恢复隐含的动作嵌入和明确的3D运动细节。基于这种混合动作信息,我们引入了一个多流空间-时空图交接网络(ST-GCN),以预测精细的语义动作动作动作特性,并采用一个语义属性绘图块,将各种关联行动属性整合成一个高层次的行动标签,以便公众全面详细了解整个动作顺序,从而能够大大改进各种应用,例如行动评估。