Motion capture is facing some new possibilities brought by the inertial sensing technologies which do not suffer from occlusion or wide-range recordings as vision-based solutions do. However, as the recorded signals are sparse and quite noisy, online performance and global translation estimation turn out to be two key difficulties. In this paper, we present TransPose, a DNN-based approach to perform full motion capture (with both global translations and body poses) from only 6 Inertial Measurement Units (IMUs) at over 90 fps. For body pose estimation, we propose a multi-stage network that estimates leaf-to-full joint positions as intermediate results. This design makes the pose estimation much easier, and thus achieves both better accuracy and lower computation cost. For global translation estimation, we propose a supporting-foot-based method and an RNN-based method to robustly solve for the global translations with a confidence-based fusion technique. Quantitative and qualitative comparisons show that our method outperforms the state-of-the-art learning- and optimization-based methods with a large margin in both accuracy and efficiency. As a purely inertial sensor-based approach, our method is not limited by environmental settings (e.g., fixed cameras), making the capture free from common difficulties such as wide-range motion space and strong occlusion.
翻译:惯性感测技术带来了一些新的机会,这些技术没有像基于愿景的解决办法那样受到封闭性或广泛记录的影响。然而,由于所记录的信号稀少,而且相当吵闹,在线性能和全球翻译估计结果成为两大难题。在本文件中,我们介绍了基于DNN的TranPose方法,即仅从6个位于90英尺以上的惰性测量单位(含全球翻译和体力)进行完全运动捕捉(同时含全球翻译和体力)的DNNN(DNN)方法。关于机构构成的估计,我们提议建立一个多阶段网络,将叶对完全联合位置作为中间结果进行估算。这种设计使估算更加容易,从而实现更高的准确性和较低的计算成本。对于全球翻译估计,我们提出了一种基于支持性的脚法和基于RNNN的方法,即用基于信任的聚变技术来强有力地解决全球翻译问题。定量和定性比较表明,我们的方法在精确性和效率两方面都超过了基于现状的学习和优化方法。这一方法使得估计更为精确,因此,这种估计更为精确,从而实现更精确和较低的计算成本和较低的计算成本。对于全球翻译来说,我们采用一种支持的移动式的固定式摄感测测测成系统的方法并不是一种固定式的固定式的固定式摄影。