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arXiv · 2502.06819

AccioScene: Compositional 3D Scene Generation via Graph Diffusion and Interaction-driven Critics

Yao Wei·Matteo Toso·Pietro Morerio·Changjae Oh·Michael Ying Yang·Alessio Del Bue
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Abstract

This paper presents a framework for generating 3D indoor scenes from text prompts. Existing methods often formulate scene synthesis as an object layout prediction problem conditioned on a single input modality, such as a text description, room shape, or scene graph. This design can lead to object collisions and limited functional plausibility, reducing its practical applicability. To address these limitations, we introduce a multi-stage pipeline that better reflects practical scene creation scenarios. Given a text prompt describing partial scene content, our method first uses graph diffusion to produce a contextually coherent scene graph and then predicts a realistic object layout. In addition, we incorporate lightweight human-object interaction priors to encourage human-centric and functional arrangements, with explicit spatial constraints to reduce interpenetration. Our approach generates coherent 3D scenes with viable layouts that better support human interaction. Experiments on the 3D-FRONT dataset demonstrate that our method achieves competitive or state-of-the-art performance compared with existing approaches, while improving the physical plausibility of generated scenes.

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Yao Wei, Matteo Toso, Pietro Morerio, Changjae Oh, Michael Ying Yang, Alessio Del Bue. 2026-06-08. AccioScene: Compositional 3D Scene Generation via Graph Diffusion and Interaction-driven Critics. https://arxiv.org/abs/2502.06819

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