88 lines
3.7 KiB
Markdown
88 lines
3.7 KiB
Markdown
## <p align="center">LION: Latent Point Diffusion Models for 3D Shape Generation<br><br> NeurIPS 2022 </p>
|
|
<div align="center">
|
|
<a href="https://www.cs.utoronto.ca/~xiaohui/" target="_blank">Xiaohui Zeng</a>  
|
|
<a href="http://latentspace.cc/" target="_blank">Arash Vahdat</a>  
|
|
<a href="https://www.fwilliams.info/" target="_blank">Francis Williams</a>  
|
|
<a href="https://zgojcic.github.io/" target="_blank">Zan Gojcic</a>  
|
|
<a href="https://orlitany.github.io/" target="_blank">Or Litany</a>  
|
|
<a href="https://www.cs.utoronto.ca/~fidler/" target="_blank">Sanja Fidler</a>  
|
|
<a href="https://karstenkreis.github.io/" target="_blank">Karsten Kreis</a>
|
|
<br> <br>
|
|
<a href="https://arxiv.org/abs/2210.06978" target="_blank">Paper</a>  
|
|
<a href="https://nv-tlabs.github.io/LION" target="_blank">Project Page</a>
|
|
</div>
|
|
|
|
<p align="center">
|
|
<img width="750" alt="Animation" src="assets/animation.gif"/>
|
|
</p>
|
|
|
|
## Update
|
|
* add pointclouds rendering code used for paper figure, see `utils/render_mitsuba_pc.py`
|
|
* When opening an issue, please add @ZENGXH so that I can reponse faster!
|
|
|
|
## Install
|
|
* Dependencies:
|
|
* CUDA 11.6
|
|
|
|
* Setup the environment
|
|
Install from conda file
|
|
```
|
|
conda env create --name lion_env --file=env.yaml
|
|
conda activate lion_env
|
|
|
|
# Install some other packages
|
|
pip install git+https://github.com/openai/CLIP.git
|
|
|
|
# build some packages first (optional)
|
|
python build_pkg.py
|
|
```
|
|
Tested with conda version 22.9.0
|
|
|
|
* Using Docker
|
|
* build the docker with `bash ./docker/build_docker.sh`
|
|
* launch the docker with `bash ./docker/run.sh`
|
|
|
|
|
|
## Demo
|
|
run `python demo.py`, will load the released text2shape model on hugging face and generate a chair point cloud. (Note: the checkpoint is not released yet, the files loaded in the `demo.py` file is not available at this point)
|
|
|
|
## Released checkpoint and samples
|
|
* will be release soon
|
|
* put the downloaded file under `./lion_ckpt/`
|
|
|
|
## Training
|
|
|
|
### data
|
|
* ShapeNet can be downloaded [here](https://github.com/stevenygd/PointFlow#dataset).
|
|
* Put the downloaded data as `./data/ShapeNetCore.v2.PC15k` *or* edit the `pointflow` entry in `./datasets/data_path.py` for the ShapeNet dataset path.
|
|
|
|
### train VAE
|
|
* run `bash ./script/train_vae.sh $NGPU` (the released checkpoint is trained with `NGPU=4` on A100)
|
|
* if want to use comet to log the experiment, add `.comet_api` file under the current folder, write the api key as `{"api_key": "${COMET_API_KEY}"}` in the `.comet_api` file
|
|
|
|
### train diffusion prior
|
|
* require the vae checkpoint
|
|
* run `bash ./script/train_prior.sh $NGPU` (the released checkpoint is trained with `NGPU=8` with 2 node on V100)
|
|
|
|
### evaluate a trained prior
|
|
* download the test data from [here](https://drive.google.com/file/d/1uEp0o6UpRqfYwvRXQGZ5ZgT1IYBQvUSV/view?usp=share_link), unzip and put it as `./datasets/test_data/`
|
|
* download the released checkpoint from above
|
|
```
|
|
checkpoint="./lion_ckpt/unconditional/airplane/checkpoints/model.pt"
|
|
bash ./script/eval.sh $checkpoint # will take 1-2 hour
|
|
```
|
|
|
|
## Evaluate the samples with the 1-NNA metrics
|
|
* download the test data from [here](https://drive.google.com/file/d/1uEp0o6UpRqfYwvRXQGZ5ZgT1IYBQvUSV/view?usp=share_link), unzip and put it as `./datasets/test_data/`
|
|
* run `python ./script/compute_score.py`
|
|
|
|
## Citation
|
|
```
|
|
@inproceedings{zeng2022lion,
|
|
title={LION: Latent Point Diffusion Models for 3D Shape Generation},
|
|
author={Xiaohui Zeng and Arash Vahdat and Francis Williams and Zan Gojcic and Or Litany and Sanja Fidler and Karsten Kreis},
|
|
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
|
|
year={2022}
|
|
}
|
|
```
|