English

WonderVerse: Extendable 3D Scene Generation with Video Generative Models

Computer Vision and Pattern Recognition 2026-03-17 v5

Abstract

We introduce \textit{WonderVerse}, a simple but effective framework for generating extendable 3D scenes. Unlike existing methods that rely on iterative depth estimation and image inpainting, often leading to geometric distortions and inconsistencies, WonderVerse leverages the powerful world-level priors embedded within video generative foundation models to create highly immersive and geometrically coherent 3D environments. Furthermore, we propose a new technique for controllable 3D scene extension to substantially increase the scale of the generated environments. Besides, we introduce a novel abnormal sequence detection module that utilizes camera trajectory to address geometric inconsistency in the generated videos. Finally, WonderVerse is compatible with various 3D reconstruction methods, allowing both efficient and high-quality generation. Extensive experiments on 3D scene generation demonstrate that our WonderVerse, with an elegant and simple pipeline, delivers extendable and highly-realistic 3D scenes, markedly outperforming existing works that rely on more complex architectures.

Keywords

Cite

@article{arxiv.2503.09160,
  title  = {WonderVerse: Extendable 3D Scene Generation with Video Generative Models},
  author = {Hao Feng and Zhi Zuo and Jia-Hui Pan and Ka-Hei Hui and Qi Dou and Jingyu Hu and Zhengzhe Liu},
  journal= {arXiv preprint arXiv:2503.09160},
  year   = {2026}
}

Comments

Accepted at CVM 2026

R2 v1 2026-06-28T22:17:15.573Z