SpatialCrafter: Single Image World Modeling with Generative 3D Proxies

📅 2026-08-27
📈 Citations: 0
Influential: 0
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🤖 AI Summary
SpatialCrafter通过引入全局3D代理和两阶段框架,解决了基于视频扩散模型的图像到场景生成中的随机幻觉、长期漂移及3D一致性不佳的问题。
📝 Abstract
Explorable image-to-scene generation is essential for applications in gaming, robotics, and virtual reality. Existing methods based on video diffusion model (VDM) commonly rely on incomplete conditioning signals such as sparse point clouds or 2D panoramas, leading to stochastic hallucinations, long-term drifts and suboptimal 3D consistency. We present SpatialCrafter, a novel two-stage framework that addresses these issues by introducing a global 3D proxy for high-fidelity image-to-scene generation. Specifically, we decompose the generation process into global proxy generation and appearance refinement. For proxy generation, we propose a Point-anchored Sparse Structure~(PaSS) Flow module that predicts a spatially aligned and geometrically consistent 3D proxy. For appearance refinement, we re-frame the VDM as a Generative Deferred Refiner which synthesizes high-frequency photorealistic details upon proxy-defined scene geometry. To better integrate the proxy with the pre-trained VDM, we introduce Parallel Geometry Injection and Proxy-Aware Corruption training strategies, which improve robustness to proxy artifacts without disrupting the pretrained generative manifold. Furthermore, as no suitable dataset exists for this explorable scene generation task, we construct a new large-scale dataset of 115K scenes. To the best of our knowledge, it is the first hybrid dataset for image-to-scene generation. Extensive experiments on both synthetic and real-world datasets show that SpatialCrafter outperforms state-of-the-art methods, mitigates long-term drift, and remains robust and consistent under rapid camera motion and extreme viewpoint changes. Code, models, and the newly constructed dataset will be publicly released. See more at https://fangchuan.github.io/SpatialCrafter/.
Problem

Research questions and friction points this paper is trying to address.

Image-to-Scene Generation
Video Diffusion Model
3D Consistency
Stochastic Hallucinations
Long-term Drift
Innovation

Methods, ideas, or system contributions that make the work stand out.

Point-anchored Sparse Structure (PaSS) Flow
Generative Deferred Refiner
Parallel Geometry Injection
Proxy-Aware Corruption