We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic components, enabling the modeling of complex, time-varying environments with varying object lifespans. We proposed a novel density control strategy in training, which enables our 4DGT to handle longer space-time input and remain efficient rendering at runtime. Our model processes 64 consecutive posed frames in a rolling-window fashion, predicting consistent 4D Gaussians in the scene. Unlike optimization-based methods, 4DGT performs purely feed-forward inference, reducing reconstruction time from hours to seconds and scaling effectively to long video sequences. Trained only on large-scale monocular posed video datasets, 4DGT can outperform prior Gaussian-based networks significantly in real-world videos and achieve on-par accuracy with optimization-based methods on cross-domain videos. Project page:this https URL
View on arXiv@article{xu2025_2506.08015,
title={ 4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos },
author={ Zhen Xu and Zhengqin Li and Zhao Dong and Xiaowei Zhou and Richard Newcombe and Zhaoyang Lv },
journal={arXiv preprint arXiv:2506.08015},
year={ 2025 }
}