Real-time 3D Multi-person Pose Estimation Demo
This repository contains 3D multi-person pose estimation demo in PyTorch. Intel OpenVINO™ backend can be used for fast inference on CPU. This demo is based on Lightweight OpenPose and Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB papers. It detects 2D coordinates of up to 18 types of keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles, as well as their 3D coordinates. It was trained on MS COCO and CMU Panoptic datasets and achieves 100 mm MPJPE (mean per joint position error) on CMU Panoptic subset. This repository significantly overlaps with https://github.com/opencv/open_model_zoo/, however contains just the necessary code for 3D human pose estimation demo.
The major part of this work was done by Mariia Ageeva, when she was the
🔝 🚀 🔥intern at Intel.
Table of Contents
- Python 3.5 (or above)
- CMake 3.10 (or above)
- C++ Compiler (g++ or MSVC)
- OpenCV 4.0 (or above)
[Optional] Intel OpenVINO for fast inference on CPU.
- Install requirements:
pip install -r requirements.txt
python setup.py build_ext
- Add build folder to
To run the demo, pass path to the pre-trained checkpoint and camera id (or path to video file):
python demo.py --model human-pose-estimation-3d.pth --video 0
Camera can capture scene under different view angles, so for correct scene visualization, please pass camera extrinsics and focal length with
--fxoptions correspondingly (extrinsics sample format can be found in data folder). In case no camera parameters provided, demo will use the default ones.
To run with OpenVINO, it is necessary to convert checkpoint to OpenVINO format:
- Set OpenVINO environment variables:
- Convert checkpoint to ONNX:
python scripts/convert_to_onnx.py --checkpoint-path human-pose-estimation-3d.pth
- Convert to OpenVINO format:
python <OpenVINO_INSTALL_DIR>/deployment_tools/model_optimizer/mo.py --input_model human-pose-estimation-3d.onnx --input=data --mean_values=data[128.0,128.0,128.0] --scale_values=data[255.0,255.0,255.0] --output=features,heatmaps,pafs
To run the demo with OpenVINO inference, pass
--use-openvino option and specify device to infer on:
python demo.py --model human-pose-estimation-3d.xml --device CPU --use-openvino --video 0