NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917362002821120 |
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| author | Yang, Yuxue Fan, Lue Shi, Ziqi Peng, Junran Wang, Feng Zhang, Zhaoxiang |
| author_facet | Yang, Yuxue Fan, Lue Shi, Ziqi Peng, Junran Wang, Feng Zhang, Zhaoxiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00393 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos Yang, Yuxue Fan, Lue Shi, Ziqi Peng, Junran Wang, Feng Zhang, Zhaoxiang Computer Vision and Pattern Recognition 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. |
| title | NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.00393 |