mlsanityData to 14 Oct 2026

FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts

arXiv 2506.02781 ↗First posted 3 Jun 2025cs.CVcited by 8

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Abstract (arXiv)

Controllability plays a crucial role in the practical applications of 3D indoor scene synthesis. Existing works either allow rough language-based control, that is convenient but lacks fine-grained scene customization, or employ graph based control, which offers better controllability but demands considerable knowledge for the cumbersome graph design process. To address these challenges, we present FreeScene, a user-friendly framework that enables both convenient and effective control for indoor scene synthesis.Specifically, FreeScene supports free-form user inputs including text description and/or reference images, allowing users to express versatile design intentions. The user inputs are adequately analyzed and integrated into a graph representation by a VLM-based Graph Designer. We then propose MG-DiT, a Mixed Graph Diffusion Transformer, which performs graph-aware denoising to enhance scene generation. Our MG-DiT not only excels at preserving graph structure but also offers broad applicability to various tasks, including, but not limited to, text-to-scene, graph-to-scene, and rearrangement, all within a single model. Extensive experiments demonstrate that FreeScene provides an efficient and user-friendly solution that unifies text-based and graph based scene synthesis, outperforming state-of-the-art methods in terms of both generation quality and controllability in a range of applications.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 45 of its 67 references that are in the map. First posted on arXiv 3 Jun 2025.

Builds on

45 of 67 references.

References · 45

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33 more
  1. 2016Semantic Scene Completion from a Single Depth Imagecited by 472
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  6. 2021ATISS: Autoregressive Transformers for Indoor Scene Synthesiscited by 200
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  8. 2020Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Imagecited by 172
  9. 2023DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesiscited by 166
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  11. 2024PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AIcited by 125
  12. 2024InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Priorcited by 117
  13. 2018Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Modelscited by 106
  14. 2023MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generationcited by 98
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  19. 2023LEGO-Net: Learning Regular Rearrangements of Objects in Roomscited by 76
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  21. 2018Deep Generative Modeling for Scene Synthesis via Hybrid Representationscited by 63
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  23. 2018Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimationcited by 51
  24. 2024Graph Diffusion Transformers for Multi-Conditional Molecular Generationcited by 45
  25. 2023SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometrycited by 39
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  28. 2021Scene Synthesis via Uncertainty-Driven Attribute Synchronizationcited by 29
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2023CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
  2. 2025GeoSceneGraph: Geometric Scene Graph Diffusion Model for Text-guided 3D Indoor Scene Synthesis
  3. 2025MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation
  4. 2024Planner3D: LLM-enhanced graph prior meets 3D indoor scene explicit regularization
  5. 2026Dependency-Aware Discrete Diffusion for Scene Graph Generation
  6. 2025ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing
  7. 20263D Scene Graph Prediction: Generating Hierarchical Models from Partially Observed Environments
  8. 2026FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow