This repository contains code corresponding to the paper Learning to Reconstruct People in Clothing from a Single RGB Camera.

Download and install DIRT: https://github.com/pmh47/dirt.

Download the neutral SMPL model from http://smplify.is.tue.mpg.de/ and place it in the assets folder.

cp <path_to_smplify>/code/models/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl assets/neutral_smpl.pkl

Download pre-trained model weights from here and place them in the weights folder.

unzip <downloads_folder>/octopus_weights.hdf5.zip -d weights

We provide scripts and sample data for single subject (infer_single.py ) and batch processing (infer_batch.py). Both scripts output usage information when executed without parameters.

We provide sample scripts for both modes:

bash run_demo.sh
bash run_batch_demo.sh

If you want to process your own data, some pre-processing steps are needed:

1. Crop your images to 1080x1080.
2. Run PGN semantic segmentation on your images.
3. Run OpenPose body_25 and face keypoint detection on your images.

Semantic segmentation and OpenPose keypoints form the input to Octopus. See data folder for sample data.

The following code may be used to stitch a texture for the reconstruction: https://github.com/thmoa/semantic_human_texture_stitching

This repository contains code corresponding to:

T. Alldieck, M. Magnor, B. L. Bhatnagar, C. Theobalt, and G. Pons-Moll. Learning to Reconstruct People in Clothing from a Single RGB Camera. In IEEE Conference on Computer Vision and Pattern Recognition, 2019.

Please cite as:

@inproceedings{alldieck19cvpr,
    title = {Learning to Reconstruct People in Clothing from a Single {RGB} Camera},
    author = {Alldieck, Thiemo and Magnor, Marcus and Bhatnagar, Bharat Lal and Theobalt, Christian and Pons-Moll, Gerard},
    booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {jun},
    year = {2019},
}

Copyright (c) 2019 Thiemo Alldieck, Technische Universität Braunschweig, Max-Planck-Gesellschaft

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