TGPOSE: A New Dual-View 3D Human Pose Estimation Framework Integrating Diffusion Models and Spatiotemporal Encoding

This article introduces the innovative dual-view 3D human pose estimation framework TGPOSE, which integrates diffusion models, graph convolutional network (GCN) spatial reasoning, and TimesNet temporal encoding technology. It significantly improves pose estimation accuracy in complex action scenarios through geometric constraints and action-specific constraints. This framework has broad application prospects in motion analysis, human-computer interaction, medical health, and other fields, driving pose estimation technology from the laboratory to practical use.

Human pose estimation is a core task in computer vision, applied in scenarios such as motion analysis and human-computer interaction. Early research focused on 2D joint localization but lost depth information; 3D pose estimation has become a hot topic, facing challenges like depth ambiguity, occlusion, and complex action variations. The combination of multi-view fusion and deep learning provides new ideas for solving these problems, and TGPOSE is an innovative achievement in this direction.

The core innovation of TGPOSE lies in the collaboration of three modules: 1. The diffusion model models the complex distribution of 3D poses and recovers reasonable poses through denoising; 2. The graph convolutional network uses the human skeleton graph structure to capture spatial dependencies between joints; 3. TimesNet extracts multi-scale temporal features to model action dynamics. The dual-view setup uses geometric constraints to alleviate depth ambiguity, optimizes 3D reconstruction with camera parameters, and enhances occlusion robustness; meanwhile, action-specific constraints are introduced to exclude unreasonable pose assumptions.

The paper verifies the effect on three challenging actions: sitting, greeting, and waiting:

• Sitting: Large-angle bending of the lower limbs is prone to errors; TGPOSE improves accuracy through GCN spatial modeling and diffusion model pose distribution learning;
• Greeting: Fast arm movements cause self-occlusion; dual-view and temporal modeling infer the positions of occluded joints;
• Waiting: Subtle shaking in static state; TimesNet's multi-scale temporal encoding captures dynamics and maintains naturalness.

TGPOSE is applicable in multiple fields:

• Sports science: Replace traditional motion capture equipment to analyze movement patterns;
• Human-computer interaction: Real-time pose input supports gesture control and VR avatar driving;
• Medical health: Rehabilitation monitoring, gait analysis, home health monitoring;
• Film and animation: Lower the threshold of motion capture to assist independent creators.

TGPOSE has limitations:

1. View dependency: Requires precise camera calibration and fixed positions;
2. Generalization ability: Accuracy decreases for unseen extreme poses;
3. Real-time performance: High computational overhead of diffusion models;
4. Multi-person scenarios: Not extended to multi-person tracking. Future directions: Robust calibration methods, enhanced generalization ability, model lightweighting, and multi-person scenario expansion.

TGPOSE integrates cutting-edge technologies to improve pose estimation accuracy in complex action scenarios, paving the way for practical application. With the maturity of algorithms and the decline in hardware costs, vision-based pose estimation will enter daily life, opening an era of more natural human-computer interaction and promoting the humanization transformation of the intelligent age.