59 lines
1.5 KiB
Markdown
59 lines
1.5 KiB
Markdown
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# Shape Generation and Completion Through Point-Voxel Diffusion
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[Project]() | [Paper]()
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Implementation of
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## Pretrained Models
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Pretrained models can be accessed [here](https://www.dropbox.com/s/a3xydf594fzaokl/cifar10_pretrained.rar?dl=0).
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## Requirements:
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Make sure the following environments are installed.
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```
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python==3.6
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pytorch==1.4.0
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torchvision==0.5.0
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cudatoolkit==10.1
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matplotlib==2.2.5
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tqdm==4.32.1
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open3d==0.9.0
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```
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The code was tested on Unbuntu with Titan RTX.
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## Training on CIFAR-10:
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```bash
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$ python train_cifar.py
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```
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Please refer to the python file for optimal training parameters.
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## Results
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Some generative results are as follows.
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<p float="left">
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<img src="example/cifar_gen.png" width="300"/>
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<img src="example/lsun_gen.png" width="300"/>
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</p>
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## Reference
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```
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@inproceedings{han2020joint,
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title={Joint Training of Variational Auto-Encoder and Latent Energy-Based Model},
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author={Han, Tian and Nijkamp, Erik and Zhou, Linqi and Pang, Bo and Zhu, Song-Chun and Wu, Ying Nian},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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pages={7978--7987},
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year={2020}
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}
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```
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## Acknowledgement
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For any questions related to codes and experiment setting, please contact Linqi (Alex) Zhou (alexzhou907@gmail.com). For questions related to model and algorithm in the paper, please contact Tian Han (hantian@ucla.edu). Thanks to [@Tian Han ](https://github.com/hthth0801?tab=repositories) and [@Erik Njikamp](https://github.com/enijkamp) for their colloboration and guidance.
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