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hw-liang committed Apr 2, 2024
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## Static Assets Preparation

We release a set of pre-generated static assets in `data/` directory. During training we keep the static 3D Gaussians fixed and only optimize the deformation modules. We refered to the first two stages of [4D-fy](https://github.com/sherwinbahmani/4dfy) to generate the static 3D objects. Then we convert them to point clouds (in `data/`) which are used to initialize 3D Gaussians. Thanks the authors for sharing their awesome codebases!
We release a set of pre-generated static assets in `data/` directory. During training we keep the static 3D Gaussians fixed and only optimize the deformation modules. We refered to the first two stages of [4D-fy](https://github.com/sherwinbahmani/4dfy) to generate the static 3D objects. Then we convert them to point clouds (in `data/`) which are used to initialize 3D Gaussians. Thanks the authors for sharing their awesome work!

#### example case
#### Example case
```
# cd /path_to_4dfy/
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```


## Acknowledgement

This work is built on many amazing research works and open-source projects. Thanks to all the authors for sharing!

- https://github.com/sherwinbahmani/4dfy
- https://github.com/hustvl/4DGaussians
- https://github.com/dreamgaussian/dreamgaussian
- https://github.com/graphdeco-inria/gaussian-splatting
- https://github.com/graphdeco-inria/diff-gaussian-rasterization
- https://github.com/threestudio-project/threestudio

## Citation

If you find this repository/work helpful in your research, please consider citing the paper and starring the repo ⭐.
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