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ICON: Implicit Clothed humans Obtained from Normals

Yuliang Xiu · Jinlong Yang · Dimitrios Tzionas · Michael J. Black

CVPR 2022

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PyTorch Lightning Google Colab

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Table of Contents
  1. Who needs ICON
  2. Instructions
  3. Running Demo
  4. Citation


Who needs ICON?

  • If you want to Train & Evaluate on PIFu / PaMIR / ICON using your own data, please check dataset.md to prepare dataset, training.md for training, and evaluation.md for benchmark evaluation.

  • Given a raw RGB image, you could get:

    • image (png):
      • segmented human RGB
      • normal maps of body and cloth
      • pixel-aligned normal-RGB overlap
    • mesh (obj):
      • SMPL-(X) body from PyMAF, PIXIE, PARE, HybrIK, BEV
      • 3D clothed human reconstruction
      • 3D garments (requires 2D mask)
    • video (mp4):
      • self-rotated clothed human
Intermediate Results
ICON's intermediate results
Iterative Refinement
ICON's SMPL Pose Refinement
Final Results
Image -- overlapped normal prediction -- ICON -- refined ICON
3D Garment
3D Garment extracted from ICON using 2D mask

Instructions


Running Demo

cd ICON

# model_type:
#   "pifu"            reimplemented PIFu
#   "pamir"           reimplemented PaMIR
#   "icon-filter"     ICON w/ global encoder (continous local wrinkles)
#   "icon-nofilter"   ICON w/o global encoder (correct global pose)
#   "icon-keypoint"   ICON w/ relative-spatial encoding (insight from KeypointNeRF)

python -m apps.infer -cfg ./configs/icon-filter.yaml -gpu 0 -in_dir ./examples -out_dir ./results -export_video -loop_smpl 100 -loop_cloth 200 -hps_type pixie

More Qualitative Results

Comparison
Comparison with other state-of-the-art methods
extreme
Predicted normals on in-the-wild images with extreme poses


Citation

@inproceedings{xiu2022icon,
  title     = {{ICON}: {I}mplicit {C}lothed humans {O}btained from {N}ormals},
  author    = {Xiu, Yuliang and Yang, Jinlong and Tzionas, Dimitrios and Black, Michael J.},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2022},
  pages     = {13296-13306}
}

Acknowledgments

We thank Yao Feng, Soubhik Sanyal, Qianli Ma, Xu Chen, Hongwei Yi, Chun-Hao Paul Huang, and Weiyang Liu for their feedback and discussions, Tsvetelina Alexiadis for her help with the AMT perceptual study, Taylor McConnell for her voice over, Benjamin Pellkofer for webpage, and Yuanlu Xu's help in comparing with ARCH and ARCH++.

Special thanks to Vassilis Choutas for sharing the code of bvh-distance-queries

Here are some great resources we benefit from:

Some images used in the qualitative examples come from pinterest.com.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.860768 (CLIPE Project).

Contributors

Kudos to all of our amazing contributors! ICON thrives through open-source. In that spirit, we welcome all kinds of contributions from the community.

Contributor avatars are randomly shuffled.


License

This code and model are available for non-commercial scientific research purposes as defined in the LICENSE file. By downloading and using the code and model you agree to the terms in the LICENSE.

Disclosure

MJB has received research gift funds from Adobe, Intel, Nvidia, Meta/Facebook, and Amazon. MJB has financial interests in Amazon, Datagen Technologies, and Meshcapade GmbH. While MJB was a part-time employee of Amazon during this project, his research was performed solely at, and funded solely by, the Max Planck Society.

Contact

For more questions, please contact icon@tue.mpg.de

For commercial licensing, please contact ps-licensing@tue.mpg.de