fabro66/GAST-Net-3DPoseEstimation

GAST-Net was a relevant research contribution when released (~2020), introducing graph attention to the 'lifting' problem (2D-to-3D pose). However, with 326 stars and zero current velocity over a 5-year lifespan, the project is effectively a legacy reference implementation. The 3D pose estimation space has since moved toward Transformer-based architectures (e.g., MixSTE, MHFormer) and diffusion models which provide superior temporal consistency and accuracy. Defensibility is low because the core algorithm is easily reproducible and has been superseded by more modern architectures in libraries like MMPose. Frontier labs (Google/MediaPipe, Meta/Ego4D) have already integrated more advanced 3D lifting capabilities into their platforms, making this specific implementation obsolete for production use. It remains useful primarily as a historical benchmark for researchers studying GNN applications in vision.