MPCTrans: Multi-Perspective Cue-Aware Joint Relationship Representation for 3D Hand Pose Estimation via Swin...

Area of Science:

• Computer Vision
• Machine Learning
• Robotics

Background:

• 3D Hand Pose Estimation (HPE) from depth images is crucial but challenging due to viewpoint variations and occlusions.
• Existing methods struggle to capture comprehensive hand appearance information effectively.

Purpose of the Study:

• To develop a novel approach for accurate 3D HPE that overcomes limitations of current methods.
• To introduce a multi-perspective cue-aware joint relationship representation using Swin Transformer (MPCTrans).

Main Methods:

• Proposed three novel modules: Adaptive Virtual Multi-viewpoint (AVM), Hierarchy Feature Estimation (HFE), and Virtual Viewpoint Evaluation (VVE).
• AVM generates informative virtual views by adaptively adjusting viewpoint angles.
• HFE estimates keypoints via hierarchical feature extraction, and VVE evaluates virtual viewpoints.
• Utilized Swin Transformer backbone for long-range semantic joint relationship extraction.

Main Results:

• MPCTrans demonstrated state-of-the-art performance on four challenging benchmark datasets.
• The model effectively learns multi-perspective cues and essential information from hand depth images.
• Achieved superior accuracy in locating and predicting hand keypoints.

Conclusions:

• MPCTrans offers a robust solution for 3D hand pose estimation from depth images.
• The proposed multi-viewpoint strategy and Swin Transformer integration significantly improve accuracy and handle complex scenarios.
• This work advances the field of 3D HPE with a novel and effective representation learning approach.