Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas

• URL: http://arxiv.org/abs/2603.28980v1
• Date: Mon, 30 Mar 2026 20:26:28 GMT
• Title: Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas
• Abstract summary: We present Stepper, a unified framework for text-driven immersive 3D scene synthesis.<n>Stepper is trained on a new large-scale, multi-view panorama dataset.<n>It achieves state-of-the-art fidelity and structural consistency, outperforming prior approaches.
• Score: 101.58587238272462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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