MEMformer: Transformer-based 3D Human Motion Estimation from MoCap Markers
Jinhui Luan, Haiyong Jiang, Junqi Diao, Ying Wang, Jun Xiao · 2022
We address the problem of 3D human motion estimation from original MoCap optical markers. The original markers are noisy, disordered, and unlabeled, hence recovering 3D human motion from them is non-trivial. Existing works are either time-consuming or assuming the knowledge of the marker labels. We address these problems by presenting an end-to-end method for 3D human motion estimation by leveraging the capability of Transformer to model long-range dependencies. The method takes original markers as inputs and learns joint poses with a Transformer-like architecture. Experimental results show that our method is able to achieve better than centimeter-level errors.