[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"workflow-asset-8d12bfc9":3,"seo:featured-workflow:8d12bfc9-4ddd-11f1-9bc6-00163e2b0d79:es":86,"workflow-related-asset-8d12bfc9-8d12bfc9-4ddd-11f1-9bc6-00163e2b0d79":87},{"id":4,"uuid":5,"slug":6,"title":7,"description":8,"author_id":9,"author_name":10,"author_avatar":11,"token_estimate":12,"time_saved":12,"model_used":11,"fork_count":12,"vote_count":12,"view_count":12,"parent_id":12,"parent_uuid":11,"lang_type":13,"steps":14,"tags":21,"has_voted":27,"visibility":17,"share_token":11,"is_featured":12,"content_hash":28,"asset_kind":29,"target_tools":30,"install_mode":34,"entrypoint":18,"risk_profile":35,"dependencies":37,"verification":46,"agent_metadata":49,"agent_fit":62,"trust":74,"provenance":83,"created_at":85,"updated_at":85},3246,"8d12bfc9-4ddd-11f1-9bc6-00163e2b0d79","asset-8d12bfc9","MMPose — OpenMMLab Pose Estimation Toolbox","MMPose provides a modular framework for 2D and 3D pose estimation covering human body, hand, face, and animal keypoint detection with 30+ state-of-the-art methods.","8a911193-3180-11f1-9bc6-00163e2b0d79","AI Open Source","",0,"en",[15],{"id":16,"step_order":17,"title":18,"description":11,"prompt_template":19,"variables":11,"depends_on":20,"expected_output":11},3809,1,"MMPose Estimation","# MMPose — OpenMMLab Pose Estimation Toolbox\n\n## Quick Use\n```bash\npip install mmpose mmengine mmcv mmdet\npython demo\u002Ftopdown_demo_with_mmdet.py \n    demo\u002Fmmdetection_cfg\u002Frtmdet_m_640-8xb32_coco-person.py \n    https:\u002F\u002Fdownload.openmmlab.com\u002Fmmpose\u002Fv1\u002Fprojects\u002Frtmposev2\u002Frtmpose-m_simcc-body7_pt-body7_420e-256x192.pth \n    --input demo\u002Fresources\u002Fdemo.jpg \n    --output-root vis_results\u002F\n```\n\n## Introduction\nMMPose is a comprehensive pose estimation toolbox from the OpenMMLab ecosystem. It supports diverse tasks from human body keypoints to hand gesture recognition and animal pose tracking, all through a consistent modular API backed by PyTorch.\n\n## What MMPose Does\n- Estimates 2D and 3D keypoints for human body, hands, face, and animals\n- Implements 30+ methods including HRNet, RTMPose, and ViTPose\n- Provides top-down and bottom-up pose estimation pipelines\n- Supports whole-body pose estimation combining body, hand, and face\n- Integrates with MMDetection for person detection before pose estimation\n\n## Architecture Overview\nMMPose follows a top-down or bottom-up paradigm. Top-down first detects each person with a bounding box (via MMDetection), then estimates keypoints within each box. Bottom-up detects all keypoints simultaneously and groups them by person. Both approaches use configurable backbones, heads, and codec modules managed by MMEngine.\n\n## Self-Hosting & Configuration\n- Install mmpose, mmengine, mmcv, and optionally mmdet via pip\n- Download model checkpoints from the MMPose model zoo\n- Use config files to select backbone, keypoint head, and dataset\n- Set input resolution to balance speed and accuracy\n- Deploy with MMDeploy for ONNX or TensorRT inference\n\n## Key Features\n- RTMPose models achieve real-time performance at high accuracy\n- Unified framework for body, hand, face, and animal keypoints\n- Extensive model zoo with pre-trained weights on COCO, MPII, and more\n- Modular codec system for keypoint encoding and decoding\n- Built-in visualization with skeleton overlay on images and video\n\n## Comparison with Similar Tools\n- **MediaPipe** — optimized for mobile and web but closed ecosystem; MMPose offers more research flexibility\n- **OpenPose** — pioneered real-time pose but is slower; RTMPose in MMPose is faster and more accurate\n- **Detectron2** — supports keypoint detection but with fewer pose-specific methods\n- **AlphaPose** — strong real-time performance but narrower scope than MMPose\n\n## FAQ\n**Q: Can MMPose track poses across video frames?**\nA: MMPose handles per-frame estimation. Combine with a tracker like ByteTrack for temporal tracking.\n\n**Q: Does it support 3D pose estimation?**\nA: Yes. MMPose includes 3D pose methods that lift 2D keypoints into 3D coordinates.\n\n**Q: What is RTMPose?**\nA: RTMPose is a real-time pose estimation model in MMPose that achieves state-of-the-art speed-accuracy tradeoffs.\n\n**Q: Can I train on custom keypoint definitions?**\nA: Yes. Define a custom dataset class with your keypoint schema and skeleton connectivity.\n\n## Sources\n- https:\u002F\u002Fgithub.com\u002Fopen-mmlab\u002Fmmpose\n- 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