PanoWorld: Geometry-Consistent Panoramic Video World Modeling
arXiv cs.CV·Le Jiang, Xiangyu Bai, Bishoy Galoaa, Shayda Moezzi, Caleb James Lee, Tooba Imtiaz, Edmund Yeh, Jennifer Dy, Yanzhi Wang, Sarah Ostadabbas
4d agoQuick Take
PanoWorld generates geometry-consistent panoramic videos from single images, enhancing depth and motion accuracy.
Key Points
- Addresses depth and motion inconsistencies in panoramic videos.
- Introduces depth and trajectory consistency regularizers.
- PanoGeo dataset supports training and evaluation.
📖 Reader Mode
~2 min readAbstract:We present PanoWorld, a panoramic video world model that generates geometry-consistent 360$\degree$ video from a single image and a caption. Existing panoramic video methods optimize primarily for visual realism and do not explicitly constrain the underlying 3D scene state, producing outputs that appear plausible yet exhibit inconsistent depth, broken correspondences, and implausible motion across the spherical surface. We address this gap by framing panoramic video generation as a geometry- and dynamics-consistent latent state modeling problem rather than pure visual synthesis. Building on a pre-trained perspective video world model, we introduce two lightweight regularizers: a depth consistency loss against pseudo ground-truth panoramic depth, and a trajectory consistency loss that supervises the 3D world-frame positions of tracked points across time. We further apply spherical-geometry-aware adaptation to the conditioning and positional encoding. We additionally introduce PanoGeo, a unified geometry-aware panoramic video dataset with consistent depth, trajectory, and prompt annotations across diverse real and synthetic sources, used for both training and stratified evaluation. Experiments show that PanoWorld improves geometric consistency over prior panoramic generation methods while maintaining competitive visual realism, establishing that panoramic video generation must be treated as a geometric modeling problem to support the holistic spatial understanding requirements of embodied AI applications. Code is available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.15391 [cs.CV] |
| (or arXiv:2605.15391v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15391 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sarah Ostadabbas [view email]
[v1]
Thu, 14 May 2026 20:24:23 UTC (7,755 KB)
— Originally published at arxiv.org
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