🎞️MoVieS

MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second

Chenguo Lin^1*, Yuchen Lin^1,3*, Panwang Pan^2†,
Yifan Yu², Honglei Yan², Katerina Fragkiadaki³, Yadong Mu¹

¹Peking University, ²ByteDance, ³Carnegie Mellon University

This repository contains the official implementation of the paper: MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second. MoVieS is a feed-forward framework that jointly reconstructs appearance, geometry and motion for 4D scene perception from monocular videos in one second.

Feel free to contact me ([email protected]) or open an issue if you have any questions or suggestions.

📢 News

• 2025-07-15: This repo is initialized and MoVieS technical report is released on arXiv.

📋 TODO

• Provide source codes for inference and training.
• Provide the pretrained checkpoint.
• Provide detailed instructions for inference and training.
• Provide pre-processing scripts for training datasets.

🔧 Installation

You may need to modify the specific version of torch in settings/setup.sh according to your CUDA version. There are not restrictions on the torch version, feel free to use your preferred one.

git clone https://github.com/chenguolin/MoVieS.git
cd MoVieS
bash settings/setup.sh

📊 Dataset

• We use three static scene datasets (RealEstate10K, TartanAir and MatrixCity) and five dynamic scene datasets (PointOdyssey, DynamicReplica, Spring, VKITTI2 and Stereo4D) to train MoVieS.
• We will provide pre-processing scripts to these datasets soon. Please stay tuned or open an issue to push me to hurry up 😃.

🚀 Usage

• TODO

😊 Acknowledgement

We would like to thank the authors of DiffSplat, VGGT, NoPoSplat, and CUT3R for their great work and generously providing source codes, which inspired our work and helped us a lot in the implementation.

📚 Citation

If you find our work helpful, please consider citing:

@article{lin2025movies,
  title={MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second},
  author={Lin, Chenguo and Lin, Yuchen and Pan, Panwang and Yu, Yifan and Yan, Honglei and Fragkiadaki, Katerina and Mu, Yadong},
  journal={arXiv preprint arXiv:2507.10065},
  year={2025}
}