DASH

<h3 align="center"><strong>DASH: Self-Supervised Decomposition and 4D Hash Encoding

for Real-Time Dynamic Scene Rendering</strong></h3>

<p align="center"> <a href="">Jie Chen</a>, <a href="">Zhangchi Hu</a>, <a href="">Peixi Wu</a>, <a href="">Huyue Zhu</a>, <br> <a href="">Hebei Li</a>, <a href="">Xiaoyan Sun</a> <br> University of Science and Technology of China <br> <b>ICCV 2025</b> </p> <div align="center"> <a href='https://arxiv.org/abs/2507.19141'><img src='https://img.shields.io/badge/Paper-arXiv-red'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <a href='https://github.com/chenj02/DASH/blob/main/LICENSE'><img src='https://img.shields.io/badge/License-MIT-green'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <br> <br> </div> <p align="center"> <img src="assets/framework.png" width="100%"/> </p>

Quick Start

Dataset Preparation

To train DASH, you should download the following dataset:

• Neural 3D Video Dataset
• Technicolor dataset

We follows 4D-GS for preprocessing the Neural 3D Video dataset, and STGS for the Technicolor dataset. Thanks very much for their excellent work.

Installation

git clone https://github.com/chenj02/DASH.git
cd DASH

conda env create -f environment.yaml
conda activate DASH

pip install -e ./submodules/diff=gaussian-rasterization
pip install -e ./submodules/simple-knn

Training

bash train.sh

or

CUDA_VISIBLE_DEVICES=0 python train.py -s <input path> \
                --model_path <output path> \
                --conf <config path> \
                --resolution 1 # for Technicolor dataset

Render

bash render.sh

or

CUDA_VISIBLE_DEVICES=0 python render.py -s <input path> \
                --skip_train \
                --model_path <output path> \
                --conf <config path> \
                --resolution 1 # for Technicolor dataset

Evaluation

python metrics.py -m <output path>

Citation

If you find our work useful, please cite:

@inproceedings{chen2025dash,
    title={DASH: Self-Supervised Decomposition and 4D Hash Encoding for Real-Time Dynamic Scene Rendering}, 
    author={Chen, Jie and Hu, Zhangchi and Wu, Peixi and Zhu, Huyue and Li, Hebei and Sun, Xiaoyan}, 
    booktitle = {International Conference on Computer Vision (ICCV)},
    year={2025}
}

Acknowledgements

Our code is based on 4D-GS and Grid4D. We thank the authors for their excellent work!