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HMR

<p align="center"> <img src="./images/paper_picked.png" width="800px"> </p>

This is a pytorch implementation of End-to-end Recovery of Human Shape and Pose by Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik, accompanying by some famous human pose estimation networks and datasets. HMR is an end-to end framework for reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most current methods that compute 2D or 3D joint locations, HMR produce a richer and more useful mesh representation that is parameterized by shape and 3D joint angles. The main objective is to minimize the reprojection loss of keypoints, which allow model to be trained using in-the-wild images that only have ground truth 2D annotations. For visual impact, please visit the author's original video.

training step (the following links are not available now due to license limitation)

1. download the following datasets.

2. download human3.6 datasets.

3. unzip the downloaded datasets.

4. unzip the model.zip

5. config the environment by modify the src/config.py and do_train.sh

6. run ./do_train.sh directly

environment configurations.

result

<p align="center"> <img src="./images/r.png" width="800px"> </p>

reference papers

reference resources