lightweight-human-pose-estimation-3d

Light Weight Human Pose Estimation 3D Demo

input

(Image from https://pixabay.com/ja/photos/%E5%A5%B3%E3%81%AE%E5%AD%90-%E7%BE%8E%E3%81%97%E3%81%84-%E8%8B%A5%E3%81%84-%E3%83%9B%E3%83%AF%E3%82%A4%E3%83%88-5204299/)

Ailia input shape(1, 3, 256, 448)
Range:[-1, 1]

output

usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 lightweight-human-pose-estimation-3d.py

Argument --rotate3d to activate 3d-canvas-rotation-mode.

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 lightweight-human-pose-estimation-3d.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 lightweight-human-pose-estimation-3d.py --video VIDEO_PATH

Reference

Real-time 3D multi-person pose estimation demo in PyTorch. OpenVINO backend can be used for fast inference on CPU.

Framework

PyTorch 1.0

Model Format

ONNX opset = 10

Netron

lightweight-human-pose-estimation-3d.onnx.prototxt