English

NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos

Computer Vision and Pattern Recognition 2026-03-27 v2

Abstract

In this paper, we propose NeoVerse, a versatile 4D world model that is capable of 4D reconstruction, novel-trajectory video generation, and rich downstream applications. We first identify a common limitation of scalability in current 4D world modeling methods, caused either by expensive and specialized multi-view 4D data or by cumbersome training pre-processing. In contrast, our NeoVerse is built upon a core philosophy that makes the full pipeline scalable to diverse in-the-wild monocular videos. Specifically, NeoVerse features pose-free feed-forward 4D reconstruction, online monocular degradation pattern simulation, and other well-aligned techniques. These designs empower NeoVerse with versatility and generalization to various domains. Meanwhile, NeoVerse achieves state-of-the-art performance in standard reconstruction and generation benchmarks. Our project page is available at https://neoverse-4d.github.io.

Keywords

Cite

@article{arxiv.2601.00393,
  title  = {NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos},
  author = {Yuxue Yang and Lue Fan and Ziqi Shi and Junran Peng and Feng Wang and Zhaoxiang Zhang},
  journal= {arXiv preprint arXiv:2601.00393},
  year   = {2026}
}

Comments

CVPR 2026; Project Page: https://neoverse-4d.github.io