College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel). 2021 Apr 26;21(9):3029. doi: 10.3390/s21093029.
High-quality and complete human motion 4D reconstruction is of great significance for immersive VR and even human operation. However, it has inevitable self-scanning constraints, and tracking under monocular settings also has strict restrictions. In this paper, we propose a human motion capture system combined with human priors and performance capture that only uses a single RGB-D sensor. To break the self-scanning constraint, we generated a complete mesh only using the front view input to initialize the geometric capture. In order to construct a correct warping field, most previous methods initialize their systems in a strict way. To maintain high fidelity while increasing the easiness of the system, we updated the model while capturing motion. Additionally, we blended in human priors in order to improve the reliability of model warping. Extensive experiments demonstrated that our method can be used more comfortably while maintaining credible geometric warping and remaining free of self-scanning constraints.
高质量且完整的人体运动 4D 重建对于沉浸式 VR 乃至人类操作都具有重要意义。然而，它具有不可避免的自扫描限制，在单目设置下跟踪也有严格的限制。在本文中，我们提出了一种结合人体先验和动作捕捉的人体运动捕捉系统，该系统仅使用单个 RGB-D 传感器。为了打破自扫描限制，我们仅使用正面视图输入生成完整的网格来初始化几何捕获。为了构建正确的变形场，大多数先前的方法以严格的方式初始化系统。为了在提高系统易用性的同时保持高保真度，我们在捕捉运动的同时更新模型。此外，我们融合了人体先验，以提高模型变形的可靠性。广泛的实验表明，我们的方法在保持可信的几何变形的同时，可以更舒适地使用，并且不受自扫描限制的影响。
