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Official implementation of "GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers"

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# GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers

---------

## Installation
1. Create a conda environment 
    ```
    conda create --name gst python=3.10
    conda activate gst
    ```

2. Install Pytorch following the [official instructions](https://pytorch.org/). Python/Pytorch combination that was verified to work is: Python 3.10, Pytorch 2.4.1, CUDA 12.4 (Ubuntu 22.04)

3. Install [4DHumans](https://github.com/shubham-goel/4D-Humans) following the official instructions. Download the SMPL model according to the repo README instructions, then run the `demo.py` script to download the pretrained models and store them under the correct path

4. Install Gaussian splatting renderer [diff-gaussian-rasterization](https://github.com/ashawkey/diff-gaussian-rasterization)

5. Install [simple-knn](https://gitlab.inria.fr/bkerbl/simple-knn)

6. Install other requirements
    ```
    pip install -r requirements.txt
    ```

## Data preparation
To train on the [HuMMan](https://caizhongang.com/projects/HuMMan/recon.html) dataset 
1. Download the dataset following the official instruction 
2. Unzip the data and change the `HUMMAN_DATASET_ROOT` to the unzipped folder inside `scene/humman.py`

## Training
```
python train_network.py +dataset=humman
```

## Evaluation 
Download the pretrained model and config from [Huggingface](https://huggingface.co/prosperolo/GST/tree/main). Then run 
```
python eval.py path_to_model.pth path_to_config.yaml num_of_images_to_save
```
The first `num_of_images_to_save` examples will be saved inside the `eval_out` folder and will look like the image below (input image on the left, rendering in the top row, ground truth image in the bottom row)
<img src="./eval_example.png">

GST

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