metadata
license: cc-by-nc-sa-4.0
4DGT Model Card
Model Details
4DGT (4D Gaussian Transformer) is a neural network model that learns dynamic 3D Gaussian representations from monocular videos. It uses a transformer-based architecture to predict 4D Gaussians from a dynamic scenes observed from an egocentric video.
- Paper: 4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos
- Project Page: https://4dgt.github.io/
- Github: GitHub repository
Please refer to the project page and github for more details of the model.
Citation
@inproceedings{xu20254dgt,
title = {4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos},
author = {Xu, Zhen and Li, Zhengqin and Dong, Zhao and Zhou, Xiaowei and Newcombe, Richard and Lv, Zhaoyang},
journal = {arXiv preprint arXiv:2506.08015},
year = {2025}
}
Model Files
Checkpoint: 4dgt_full.pth
- Size: ~14.5 GB
- Format: PyTorch state dict
- Contents:
- The full model trained as described in the paper.
- Encoder weights (DINOv2 backbone)
- Level of Details Transformer
- 4D Gaussian Decoder
Checkpoint: 4dgt_1st_stage.pth
- Size: ~4.85 GB
- Format: PyTorch state dict
- Contents:
- The first stage model trained only using Egoexo4D dataset as described in the paper.
- Encoder weights (DINOv2 backbone)
- Vanilla Transformer, no level of details.
- 4D Gaussian Decoder
Quick Start
Please refer to 4DGT GitHub repository for the full set up.
Contact
For questions and issues, please open an issue on the GitHub repository.