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<div align="center"> <h1>SHERF: Generalizable Human NeRF from a Single Image</h1> <div> <a href="https://skhu101.github.io" target="_blank">Shoukang Hu*<sup>1</sup></a>&emsp; <a href="https://hongfz16.github.io/" target="_blank">Fangzhou Hong*<sup>1</sup></a>&emsp; <a href="https://scholar.google.com/citations?user=lSDISOcAAAAJ" target="_blank">Liang Pan<sup>1</sup></a>&emsp; Haiyi Mei<sup>2</sup>&emsp; <a href="https://scholar.google.com.hk/citations?user=jZH2IPYAAAAJ&hl=en" target="_blank">Lei Yang<sup>2</sup></a>&emsp; <a href="https://liuziwei7.github.io/" target="_blank">Ziwei Liu</a>✉<sup>1</sup>&emsp; </div> <div> <sup>1</sup>S-Lab, Nanyang Technological University&emsp; <sup>2</sup>Sensetime Research </div> <div> <sup>*</sup>Equal Contribution&emsp; <sup>✉</sup>Corresponding Author </div> <div> ICCV 2023 </div>

<strong>SHERF learns a Generalizable Human NeRF to animate 3D humans from a single image.</strong>

<div style="width: 70%; text-align: center; margin:auto;"> <img style="width:100%" src="img/SHERF_teaser.png"> <em>Figure 1. <strong>SHERF is a single image-based generalizable Human NeRF.</strong> With just one inference pass on a single image, SHERF reconstructs Human NeRF in the canonical space which can be driven and rendered for novel view and pose synthesis.</em> </div>

:open_book: For more visual results, go checkout our <a href="https://skhu101.github.io/SHERF" target="_blank">project page</a>

This repository will contain the official implementation of SHERF: Generalizable Human NeRF from a Single Image.

<div align="left">

:mega: Updates

[08/2023] Training and inference codes for RenderPeople, THuman, HuMMan and ZJU-Mocap are released.

:desktop_computer: Requirements

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NVIDIA GPUs are required for this project. We recommend using anaconda to manage the python environments.

    conda create --name sherf python=3.8
    conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
    conda install -c fvcore -c iopath -c conda-forge fvcore iopath
    conda install pytorch3d -c pytorch3d (or pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_cu113_pyt1110/download.html)
    pip install -r requirements.txt
    conda activate sherf

Set up Dataset

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RenderPeople Dataset

Please download our rendered multi-view images of RenderPeople dataset from OneDrive.

THuman Dataset

Please follow instructions of MPS-NeRF to download the THuman dataset. After that, please download our estimated SMPL Neutral parameters.

HuMMan Dataset

Please follow instructions of HuMMan-Recon to download the HuMMan dataset.

ZJU-Mocap dataset

Please follow instructions of Neural Body to download the ZJU-Mocap dataset.

Tips: If you hope to learn how to render multi-view images, You may refer to XRFeitoria, a rendering toolbox for generating synthetic data photorealistic with ground-truth annotations.

:running_woman: Inference

Download Models

The pretrained models and SMPL model are needed for inference.

The pretrained models are put in OneDrive and Baidu Pan (pin:gu1q) for downloading.

Register and download SMPL models here. Put the downloaded models in the folder smpl_models. Only the neutral one is needed. The folder structure should look like

./
├── ...
└── assets/
    ├── SMPL_NEUTRAL.pkl

cd sherf

Inference code with RenderPeople dataset

bash eval_renderpeople_512x512.sh

Inference code with THuman dataset

bash eval_THuman_512x512.sh

Inference code with HuMMan dataset

bash eval_HuMMan_640x360.sh

Inference code with ZJU-Mocap dataset

bash eval_zju_mocap_512x512.sh

:train: Training

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cd sherf

Training code with RenderPeople dataset

bash train_renderpeople_512x512.sh

Training code with THuman dataset

bash train_THuman_512x512.sh

Training code with HuMMan dataset

bash train_HuMMan_640x360.sh

Training code with ZJU_MoCap dataset

bash train_zju_mocap_512x512.sh

If you hope to evaluate the trained checkpoints, please add --test_flag True --resume CHECKPOINT.

:love_you_gesture: Citation

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If you find the codes of this work or the associated ReSynth dataset helpful to your research, please consider citing:

@article{hu2023sherf,
  title={SHERF: Generalizable Human NeRF from a Single Image},
  author={Hu, Shoukang and Hong, Fangzhou and Pan, Liang and Mei, Haiyi and Yang, Lei and Liu, Ziwei},
  journal={arXiv preprint arXiv:2303.12791},
  year={2023}
}

:newspaper_roll: License

Distributed under the S-Lab License. See LICENSE for more information.

:raised_hands: Acknowledgements

This study is supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 2 (MOE-T2EP20221-0012), NTU NAP, and under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).

This project is built on source codes shared by EG3D, MPS-NeRF and Neural Body.