ADGT: Enhancing 3D human pose estimation with attention-driven graph-transformers

Key Points

• The aim is to improve the accuracy of 3D human pose estimation using advanced attention-driven graph-transformers.
• Developed a novel model integrating attention mechanisms with graph transformers.
• Evaluated performance against standard benchmarks in 3D human pose estimation.
• Analyzed results using metrics relevant to accuracy and efficiency.
• The attention-driven model achieved a 15% improvement in accuracy compared to baseline models.
• Demonstrated a reduction in computation time by 30%, improving efficiency significantly.