This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth prediction, motion tracking, and segmentation models, offer promising capabilities. However, training a single model for comprehensive 4D understanding remains challenging. We introduce Uni4D, a multi-stage optimization framework that harnesses multiple pretrained models to advance dynamic 3D modeling, including static/dynamic reconstruction, camera pose estimation, and dense 3D motion tracking. Our results show state-of-the-art performance in dynamic 4D modeling with superior visual quality. Notably, Uni4D requires no retraining or fine-tuning, highlighting the effectiveness of repurposing visual foundation models for 4D understanding.
@article{arxiv.2503.21761,
title = {Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video},
author = {David Yifan Yao and Albert J. Zhai and Shenlong Wang},
journal= {arXiv preprint arXiv:2503.21761},
year = {2025}
}