Regular Splitting Graph Network for 3D Human Pose Estimation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Human pose estimation often models skeletons as undirected graphs.
• Existing methods primarily use first-order neighbors, limiting the capture of distant joint relationships.
• This limitation hinders the exploitation of higher-order dependencies in human skeletons.

Purpose of the Study:

• To introduce a higher-order regular splitting graph network (RS-Net) for 2D-to-3D human pose estimation.
• To enhance the capture of long-range dependencies between body joints.
• To improve the accuracy and robustness of 3D human pose estimation.

Main Methods:

• Developed RS-Net utilizing matrix splitting, weight modulation, and adjacency modulation.
• Incorporated multi-hop neighborhoods to capture long-range dependencies between body joints.
• Employed weight unsharing and a learnable modulation matrix to refine graph structure and joint feature aggregation.

Main Results:

• RS-Net demonstrated superior performance in 3D human pose estimation compared to state-of-the-art methods.
• Ablation studies confirmed the effectiveness of the proposed higher-order graph network approach.
• The model successfully captured complex relationships between distant body joints.

Conclusions:

• The proposed RS-Net effectively addresses the limitations of first-order neighbor focus in graph-based pose estimation.
• Higher-order graph convolutional networks offer significant advantages for 3D human pose estimation.
• The method provides a robust and accurate solution for reconstructing human poses in three dimensions.