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arXiv Computer Vision By Zhaorui Lin, Haohan Liu, Zhen Chen, Xiangru Huang

GRASE: Geometry-native Real-to-sim Agentic Scene Modeling from Video

Read the original on arXiv Computer Vision →

GRASE is a new agentic framework for converting real-world video into editable 3D scenes while preserving geometry and layout. It treats captured multi‑view video as a persistent reference, revising intermediate hypotheses whenever rendered evidence conflicts with the capture. By iteratively refining architectural shells, asset placement, and geometry with cross‑view feedback, GRASE produces faithful, consistent scenes that outperform existing state‑of‑the‑art methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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