KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #developer-tools #cli #codex #claude-code #react #mcp #open-source #deepseek-harness #dsh-plugin #ai

Dual-Space-NeRF

★ 78 Open GitHub ↗

[3DV-2022] The official repo for the paper "Dual-Space NeRF: Learning Animatable Avatars and Scene Lighting in Separate Spaces".

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Dual-Space NeRF
[3DV-2022] The official repo for the  paper "Dual-Space NeRF: Learning Animatable Avatars and Scene Lighting in Separate Spaces".

[[paper](https://arxiv.org/abs/2208.14851) / [video](https://youtu.be/2qk4WOO8YMw)]

![](https://raw.githubusercontent.com/zyhbili/Dual-Space-NeRF/main/demo/demo.gif)

## Model
We provide all checkpoints and ``X_smpl_vertices`` at [here](https://shanghaitecheducn-my.sharepoint.com/:f:/g/personal/zhiyh_shanghaitech_edu_cn/EnY3wUVqJtJEpusUjfG-u3UBttBNvKUP9Xh_AqG-cJ7G1w?e=5Ub4rN).

## Installation

1. Clone this repository:
   ```
   git clone https://github.com/zyhbili/Dual-Space-NeRF.git
   ```

2. Install required python packages:
   ```
   pip install -r requirements.txt
   ```
3. Download the SMPL model (neutral) from:
   ```
   https://smpl.is.tue.mpg.de/
   ```
   and modify the `_C.DATASETS.SMPL_PATH` in `configs/defaults.py`.

## Dataset

Download and unzip [ZJU_Mocap](https://github.com/zju3dv/neuralbody/blob/master/INSTALL.md#zju-mocap-dataset), then modify the `_C.DATASETS.ZJU_MOCAP_PATH` in `configs/defaults.py`.

Prepare Human3.6M following [Animatable_NeRF](https://github.com/zju3dv/animatable_nerf) and modify the `_C.DATASETS.H36M_PATH` in `configs/defaults.py`.

## Run the code
Take `ZJU-Mocap 313` as an example, other configs files are provided in `configs/{h36m,zju_mocap}`.
### Command Lines 
   Train Dual Space NeRF

```
python3 main.py -c configs/zju_mocap/313.yml --exp 313
```

Test Dual Space NeRF
```
python3 test.py -c configs/zju_mocap/313.yml --ckpt [ckpt_path.pth] --exp 313
```
### Novel pose synthesis
Download [CoreView_313_op3.zip](https://shanghaitecheducn-my.sharepoint.com/:f:/g/personal/zhiyh_shanghaitech_edu_cn/EnY3wUVqJtJEpusUjfG-u3UBttBNvKUP9Xh_AqG-cJ7G1w?e=5Ub4rN), and unzip it into ``novelpose_examples\``
```
python3 novel_pose_vis.py -c configs/zju_mocap/313.yml --ckpt ckpt/313/model_epoch_0000200.pth --exp 313_op3
```
The results are saved into ``motion_transfer/313_op3/``

For customed pose seq, you need to prepare the SMPL vertices as provided in the ZIP file and then modify the `novel_pose_dataset.vertices_dir` in `novel_pose_vis.py`

### Lighting MLP visualization
```
python3 vis_lighting.py -c configs/zju_mocap/313.yml --ckpt ckpt/313/model_epoch_0000200.pth --exp 313_lighting
```
The results are saved into ``lighting_vis/313_lighting/``

## Cite
```
@inproceedings{zhi2022dual,
  title={Dual-Space NeRF: Learning Animatable Avatars and Scene Lighting in Separate Spaces},
  author={Zhi, Yihao and Qian, Shenhan and Yan, Xinhao and Gao, Shenghua},
  booktitle = {International Conference on 3D Vision (3DV)},
  month = sep,
  year = {2022},
}
```

Dual-Space-NeRF

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README