Papers by Mingzhi Sheng

1 papers
SceneLM: 3D-Aware Language Models for Editable 3D Scene Synthesis (2026.findings-acl)

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Challenge: Existing methods for synthesising 3D scenes from a single image are text-driven and lack precise metric understanding from images.
Approach: They propose a language-model-based framework that grounds 3D scene synthesis in visual evidence by recovering an executable metric 3D layout directly from a single image.
Outcome: The proposed framework recovers an executable metric 3D layout directly from an RGB image and instantiates, places, and edits objects for iterative refinement.

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