4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos

Zhen Xu, Zhengqin Li, Zhao Dong, Xiaowei Zhou, Richard Newcombe, Zhaoyang Lv

Keywords: Image-Based Rendering, Large Reconstruction Model, 3DGS, 4DGS, Self-Supervised Training, Monocular Reconstruction

TL;DR: We propose a scalable 4D dynamic reconstruction model trained only on real-world monocular RGB videos.

Abstract: 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. 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.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 9629