Awesome Human Pose Estimation
A curated collection of papers, datasets, and resources for 2D/3D human pose estimation, mesh representation, and related computer vision tasks.
What is Awesome Human Pose Estimation?
Awesome Human Pose Estimation is a curated GitHub repository that serves as a centralized resource hub for research and development in human pose estimation. It compiles academic papers, datasets, code implementations, and benchmarks related to estimating and analyzing human body poses from images and videos. The project addresses the need for an organized, up-to-date reference for computer vision researchers and engineers working on pose-related problems.
Computer vision researchers, PhD students, and machine learning engineers focused on human pose estimation, action recognition, or 3D reconstruction. It's particularly valuable for those entering the field or staying current with state-of-the-art methods.
It saves significant time in literature review and resource gathering by providing a meticulously categorized, community-maintained collection. Unlike generic paper lists, it specializes in pose estimation, includes practical code links, and covers both foundational and cutting-edge topics.
Overview
Human Pose Estimation Related Publication
Use Cases
Best For
- Literature review for human pose estimation research
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
