3D and 4D World Modeling: A Survey
Authors: name: Lingdong Kong, name: Wesley Yang, name: Jianbiao Mei, name: Youquan Liu, name: Ao Liang, name: Dekai Zhu, name: Dongyue Lu, name: Wei Yin, name: Xiaotao Hu, name: Mingkai Jia, name: Junyuan Deng, name: Kaiwen Zhang, name: Yang Wu, name: Tianyi Yan, name: Shenyuan Gao, name: Song Wang, name: Linfeng Li, name: Liang Pan, name: Yong Liu, name: Jianke Zhu, name: Wei Tsang Ooi, name: Steven C. H. Hoi, name: Ziwei Liu

World modeling has become a cornerstone in AI research, enabling agents to
understand, represent, and predict the dynamic environments they inhabit. While
prior work largely emphasizes generative methods for 2D image and video data,
they overlook the rapidly growing body of work that leverages native 3D and 4D
representations such as RGB-D imagery, occupancy grids, and LiDAR point clouds
for large-scale scene modeling. At the same time, the absence of a standardized
definition and taxonomy for ``world models'' has led to fragmented and
sometimes inconsistent claims in the literature. This survey addresses these
gaps by presenting the first comprehensive review explicitly dedicated to 3D
and 4D world modeling and generation. We establish precise definitions,
introduce a structured taxonomy spanning video-based (VideoGen),
occupancy-based (OccGen), and LiDAR-based (LiDARGen) approaches, and
systematically summarize datasets and evaluation metrics tailored to 3D/4D
settings. We further discuss practical applications, identify open challenges,
and highlight promising research directions, aiming to provide a coherent and
foundational reference for advancing the field. A systematic summary of
existing literature is available at https://github.com/worldbench/survey