#pose estimation

BoxMOT offers state-of-the-art, flexible multi-object trackers for segmentation, detection, and pose estimation, adaptable to various hardware from CPUs to GPUs. Compatible with YOLO models, it integrates advanced ReID systems and reduces experimentation overhead with efficient data handling.

This list provides extensive resources concerning 6D object pose estimation and single-view 3D object reconstruction, plus 3D hand-object pose estimation. It excludes geometry-based methods but spotlights influential papers, open-source demos, and key research. Included are major conference works, 3D vision and robotics workshops, datasets, and relevant challenges for both researchers and developers. Additionally, it features esteemed arXiv papers and resources from premier conferences such as CVPR and ICCV.

Explore up-to-date tools for object detection and image processing with YOLO11, a cutting-edge model known for its rapid speed, accuracy, and versatility. This model supports a variety of tasks such as object detection, tracking, instance segmentation, image classification, and pose estimation. Building on the success of prior YOLO models, it delivers enhanced performance and intuitive features. Access detailed documentation, community forums, and installation options for seamless project integration. Engage with the developer community for further insights and practical applications of YOLO11.

Discover how Residual Steps Network (RSN) advances multi-person pose estimation by effectively aggregating intra-level spatial features for precise keypoint localization. The Pose Refine Machine (PRM) enhances this precision by refining keypoint positions. As a winner of the COCO 2019 Keypoint Challenge, RSN demonstrates leading performance on both COCO and MPII benchmarks. Now part of the MMPose framework, RSN offers a substantial resource for pose estimation research and development.

DeepLabCut is a comprehensive toolkit offering precise, markerless pose estimation for various animal species and behaviors. It employs advanced deep learning techniques to deliver robust performance even with limited training data and video compression. Utilizing a PyTorch backend, it provides easy installation, user flexibility, and enhanced performance. Its capabilities in real-time support and tracking multiple animals expand its utility in fields like neuroscience, ecology, and AI research. The active open-source community ensures its continuous development and support.

GDRNPP_BOP2022 offers comprehensive tools and models for 6D pose estimation as featured in the BOP Challenge 2022. The repository includes resources for dataset setup, model downloads, and uses yolox for advanced detection. It provides upgraded Pose Estimation methods with a Convnext backbone and dual mask capabilities and includes depth-based techniques for enhanced pose refinement.