Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy

3D human pose estimation is a fundamental problem in artificial intelligence, and it has wide applications in AR/VR, HCI and robotics. However, human pose estimation from point clouds still suffers from noisy points and estimated jittery artifacts because of handcrafted-based point cloud sampling and single-frame-based estimation strategies. In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. To sample effective point clouds from input, we design a differentiable point cloud sampling method built on density-guided attention mechanism. To avoid the jitter caused by previous 3D human pose estimation problems, we adopt temporal information to obtain more stable results. Experiments on the ITOP dataset and the NTU-RGBD dataset demonstrate that all of our contributed components are effective, and our method can achieve state-of-the-art performance.

Authors

• Zihao Zhang
• Lei Hu Department of Radiology, Guangdong Provincial People’s Hospital, Southern Medical University
• Xiaoming Deng Institute of Software, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Shihong Xia

Keywords

• Computer Vision: 2D and 3D Computer Vision
• Computer Vision: Motion and Tracking
• Machine Learning: Deep Learning