Learning Neural Volumetric Representations of Dynamic Humans in Minutes

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Learning Neural Volumetric Representations of Dynamic Humans in Minutes

Chen Geng*, Sida Peng*, Zhen Xu*, Hujun Bao, Xiaowei Zhou (* denotes equal contribution)

CVPR 2023

Installation

See here.

Reproducing results in the paper

We provide two scripts to help reproduce the results shown in the paper.

After installing the environment and the dataset, for evaluation on the ZJU-MoCap dataset, run:

sh scripts/eval_zjumocap.sh

For evaluation on the MonoCap dataset, run:

sh scripts/eval_monocap.sh

Evaluation on ZJU-MoCap

Let's take "377" as an example.

Training on ZJU-MoCap can be done by running.

export name=377
python train_net.py --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS} 

Evaluation can be done by running:

export name=377
python run.py --type evaluate --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS}

Evaluation on MonoCap

Let's take "lan" as an example.

Training on Monocap can be done by running:

export name=lan
python train_net.py --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS} 

Evaluation can be done by running:

export name=lan
python run.py --type evaluate --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS}

TODO List

This repository currently serves as the release of the technical paper's implementation and will undergo future updates (planned below) to enhance user-friendliness. We warmly welcome and appreciate any contributions.

• Instruction on running on custom datasets
• Add support for further acceleration using CUDA
• Add a Google Colab notebook demo

Bibtex

If you find the repo useful for your research, please consider citing our paper:

@inproceedings{instant_nvr,
    title={Learning Neural Volumetric Representations of Dynamic Humans in Minutes},
    author={Chen Geng and Sida Peng and Zhen Xu and Hujun Bao and Xiaowei Zhou},
    booktitle={CVPR},
    year={2023}
}