Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915972719312896 |
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| author | Wimbauer, Felix Manhardt, Fabian Oechsle, Michael Kalischek, Nikolai Rupprecht, Christian Cremers, Daniel Tombari, Federico |
| author_facet | Wimbauer, Felix Manhardt, Fabian Oechsle, Michael Kalischek, Nikolai Rupprecht, Christian Cremers, Daniel Tombari, Federico |
| contents | The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with vast potential in AR/VR and world modeling. While panoramic images have proven effective for scene initialization, existing approaches suffer from a trade-off between visual fidelity and explorability: autoregressive expansion suffers from context drift, while panoramic video generation is limited to low resolution. We present Stepper, a unified framework for text-driven immersive 3D scene synthesis that circumvents these limitations via stepwise panoramic scene expansion. Stepper leverages a novel multi-view 360° diffusion model that enables consistent, high-resolution expansion, coupled with a geometry reconstruction pipeline that enforces geometric coherence. Trained on a new large-scale, multi-view panorama dataset, Stepper achieves state-of-the-art fidelity and structural consistency, outperforming prior approaches, thereby setting a new standard for immersive scene generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28980 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas Wimbauer, Felix Manhardt, Fabian Oechsle, Michael Kalischek, Nikolai Rupprecht, Christian Cremers, Daniel Tombari, Federico Computer Vision and Pattern Recognition The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with vast potential in AR/VR and world modeling. While panoramic images have proven effective for scene initialization, existing approaches suffer from a trade-off between visual fidelity and explorability: autoregressive expansion suffers from context drift, while panoramic video generation is limited to low resolution. We present Stepper, a unified framework for text-driven immersive 3D scene synthesis that circumvents these limitations via stepwise panoramic scene expansion. Stepper leverages a novel multi-view 360° diffusion model that enables consistent, high-resolution expansion, coupled with a geometry reconstruction pipeline that enforces geometric coherence. Trained on a new large-scale, multi-view panorama dataset, Stepper achieves state-of-the-art fidelity and structural consistency, outperforming prior approaches, thereby setting a new standard for immersive scene generation. |
| title | Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.28980 |