# 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.

## Install

Save in your project root:

# MMPose — OpenMMLab Pose Estimation Toolbox

## Quick Use
```bash
pip install mmpose mmengine mmcv mmdet
python demo/topdown_demo_with_mmdet.py 
    demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py 
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev2/rtmpose-m_simcc-body7_pt-body7_420e-256x192.pth 
    --input demo/resources/demo.jpg 
    --output-root vis_results/
```

## Introduction
MMPose 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.

## What MMPose Does
- Estimates 2D and 3D keypoints for human body, hands, face, and animals
- Implements 30+ methods including HRNet, RTMPose, and ViTPose
- Provides top-down and bottom-up pose estimation pipelines
- Supports whole-body pose estimation combining body, hand, and face
- Integrates with MMDetection for person detection before pose estimation

## Architecture Overview
MMPose 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.

## Self-Hosting & Configuration
- Install mmpose, mmengine, mmcv, and optionally mmdet via pip
- Download model checkpoints from the MMPose model zoo
- Use config files to select backbone, keypoint head, and dataset
- Set input resolution to balance speed and accuracy
- Deploy with MMDeploy for ONNX or TensorRT inference

## Key Features
- RTMPose models achieve real-time performance at high accuracy
- Unified framework for body, hand, face, and animal keypoints
- Extensive model zoo with pre-trained weights on COCO, MPII, and more
- Modular codec system for keypoint encoding and decoding
- Built-in visualization with skeleton overlay on images and video

## Comparison with Similar Tools
- **MediaPipe** — optimized for mobile and web but closed ecosystem; MMPose offers more research flexibility
- **OpenPose** — pioneered real-time pose but is slower; RTMPose in MMPose is faster and more accurate
- **Detectron2** — supports keypoint detection but with fewer pose-specific methods
- **AlphaPose** — strong real-time performance but narrower scope than MMPose

## FAQ
**Q: Can MMPose track poses across video frames?**
A: MMPose handles per-frame estimation. Combine with a tracker like ByteTrack for temporal tracking.

**Q: Does it support 3D pose estimation?**
A: Yes. MMPose includes 3D pose methods that lift 2D keypoints into 3D coordinates.

**Q: What is RTMPose?**
A: RTMPose is a real-time pose estimation model in MMPose that achieves state-of-the-art speed-accuracy tradeoffs.

**Q: Can I train on custom keypoint definitions?**
A: Yes. Define a custom dataset class with your keypoint schema and skeleton connectivity.

## Sources
- https://github.com/open-mmlab/mmpose
- https://mmpose.readthedocs.io/

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Author: AI Open Source