Weakly supervised adversarial learning for 3D human pose estimation from point clouds

Point clouds-based 3D human pose estimation that aims to recover the 3D locations of human skeleton joints plays an important role in many AR/VR applications. The success of existing methods is generally built upon large scale data annotated with 3D human joints. However, it is a labor-intensive and error-prone process to annotate 3D human joints from input depth images or point clouds, due to the self-occlusion between body parts as well as the tedious annotation process on 3D point clouds. Meanwhile, it is easier to construct human pose datasets with 2D human joint annotations on depth images. To address this problem, we present a weakly supervised adversarial learning framework for 3D human pose estimation from point clouds. Compared to existing 3D human pose estimation methods from depth images or point clouds, we exploit both the weakly supervised data with only annotations of 2D human … Download paper here

Recommended citation: Zihao Zhang, Lei Hu, Xiaoming Deng, and Shihong Xia. “Weakly supervised adversarial learning for 3D human pose estimation from point clouds.” IEEE transactions on visualization and computer graphics 26, no. 5 (2020): 1851-1859.