Full-Reference Motion Quality Assessment Based on Efficient Monocular Parametric 3D Human Body Reconstruction

Yuxin Huang, Yiwei Yuan, Xiangyu Zeng, Ling Xie, Yiyu Fu, Guanghui Yue, Baoquan Zhao

Abstract: Human motion capture and analysis are pivotal to a wide spectrum of killer applications in various domains such as sports, performing arts, diagnostic tests in physical medicine, rehabilitation, and figure training. However, automatic reconstruction, assessment, and visualization of human motions from a monocular video are still suffering from grand challenges that are inadequately addressed by existing studies. In this paper, we present a novel full-reference human motion quality assessment and visualization system based on monocular parametric 3D human body reconstruction. Specifically, our method first reconstructs a 3D parametric model from each sampled frame of a monocular video and harvests a physically plausible motion sequence using the proposed optimization scheme; Secondly, a full-reference assessment metric is designed to evaluate the consistency between the reconstructed motion and the reference; Finally, a new interactive visualization system is developed to facilitate multi-grained motion quality evaluation and visual analysis. Extensive quantitative and qualitative experiments demonstrate the effectiveness and superiority of the proposed method.