https://github.com/zju3dv/instant-nvr
[CVPR 2023] Code for "Learning Neural Volumetric Representations of Dynamic Humans in Minutes"
https://github.com/zju3dv/instant-nvr
computer-vision cvpr2023 digital-human nerf neural-rendering view-synthesis
Last synced: about 1 year ago
JSON representation
[CVPR 2023] Code for "Learning Neural Volumetric Representations of Dynamic Humans in Minutes"
- Host: GitHub
- URL: https://github.com/zju3dv/instant-nvr
- Owner: zju3dv
- License: other
- Created: 2023-06-12T15:44:57.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2024-05-22T04:46:20.000Z (about 2 years ago)
- Last Synced: 2025-05-12T22:38:54.888Z (about 1 year ago)
- Topics: computer-vision, cvpr2023, digital-human, nerf, neural-rendering, view-synthesis
- Language: Python
- Homepage: https://zju3dv.github.io/instant_nvr
- Size: 16.9 MB
- Stars: 159
- Watchers: 15
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Learning Neural Volumetric Representations of Dynamic Humans in Minutes
### [Project Page](https://zju3dv.github.io/instant_nvr) | [Video](https://zju3dv.github.io/instant_nvr) | [Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Geng_Learning_Neural_Volumetric_Representations_of_Dynamic_Humans_in_Minutes_CVPR_2023_paper.pdf) | [Data](https://github.com/zju3dv/instant-nvr/blob/master/docs/install.md#set-up-datasets)
> [Learning Neural Volumetric Representations of Dynamic Humans in Minutes](https://zju3dv.github.io/instant_nvr)
>
> Chen Geng\*, Sida Peng\*, Zhen Xu\*, Hujun Bao, Xiaowei Zhou (* denotes equal contribution)
>
> CVPR 2023
## Installation
See [here](./docs/install.md).
## 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:
```shell
sh scripts/eval_zjumocap.sh
```
For evaluation on the MonoCap dataset, run:
```shell
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.
```shell
export name=377
python train_net.py --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS}
```
Evaluation can be done by running:
```shell
export name=377
python run.py --type evaluate --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS}
```
## Evaluation on MonoCap
Let's take "lan" as an example.
Training on Monocap can be done by running:
```shell
export name=lan
python train_net.py --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS}
```
Evaluation can be done by running:
```shell
export name=lan
python run.py --type evaluate --cfg_file configs/inb/inb_${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.
- [x] Instruction on running on custom datasets (Kudos to [@tian42chen](https://github.com/tian42chen)!!)
- [ ] 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}
}
```