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https://gafniguy.github.io/4D-Facial-Avatars/

Dynamic Neural Radiance Fields for Monocular 4D Facial Avater Reconstruction
https://gafniguy.github.io/4D-Facial-Avatars/

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Dynamic Neural Radiance Fields for Monocular 4D Facial Avater Reconstruction

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## NeRFace: Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction [CVPR 2021 Oral Presentation]



*Guy Gafni1, Justus Thies1, Michael Zollhöfer2, Matthias Nießner1*



1 Technichal University of Munich, 2Facebook Reality Labs



![teaser](https://justusthies.github.io/posts/nerface/teaser.jpg)



ArXiv:  PDF,  abs



Project Page & Video: https://gafniguy.github.io/4D-Facial-Avatars/



**If you find our work useful, please include the following citation:**



```

@InProceedings{Gafni_2021_CVPR,

    author    = {Gafni, Guy and Thies, Justus and Zollh{\"o}fer, Michael and Nie{\ss}ner, Matthias},

    title     = {Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction},

    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

    month     = {June},

    year      = {2021},

    pages     = {8649-8658}

}

```



**Dataset and License**



Dataset is available for download [here](https://syncandshare.lrz.de/getlink/fiFbKE8dEDWYENSr75L9WG/nerface_dataset.zip). Please do not use it for commercial use and respect the license attached within the zip file. (The material in this repository is licensed under an Attribution-NonCommercial-ShareAlike 4.0 International license). 



If you make use of this dataset or code, please cite our paper. 

MIT License applies for the code.



**Code Structure**

The nerf code is heavily based on this repo by Krishna Murthy. Thank you! 



Installation etc:

Originally the project used torch 1.7.1, but this should also run with torch 1.9.0 (cuda 11).

If you get any errors related to `torchsearchsorted`, ignore this module and don't bother installing it, and comment out its imports. Its functionality is impmlemented in pytorch.

These two are interchangeable:

```

    #inds = torchsearchsorted.searchsorted(cdf, u, side="right")  # needs compilationo of torchsearchsorted

    inds = torch.searchsorted(cdf.detach(), u, right=True)  # native to pytorch 

```



The main training and testing scripts are `train_transformed_rays.py` and `eval_transformed_rays.py`, respectively. They are in the main working folder which is in `nerface_code/nerf-pytorch/` 



The training script expects a path to a config file, e.g.:



`python train_transformed_rays.py --config ./path_to_data/person_1/person_1_config.yml `



The eval script will also take a path to a model checkpoint and a folder to save the rendered images:



`python eval_transformed_rays.py --config ./path_to_data/person_1/person_1_config.yml --checkpoint /path/to/checkpoint/checkpoint400000.ckpt --savedir ./renders/person_1_rendered_frames`



The config file must refer to a dataset to use in `dataset.basedir`. Download the dataset from the .zip shared above, and place it in the nerf-pytorch directory. 



If you have your own video sequence including per frame tracking, you can see how I create the json's for training in the `real_to_nerf.py` file (main function). This does not include the code for tracking, which unfortunately I cannot publish. 



Don't hesitate to contact [guy.gafni at tum.de] for additional questions, or open an issue here.



Code for the webpage is borrowed from the ScanRefer project.