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

Tongyuan Bai, Wangyuanfan Bai, Dong Chen, Tieru Wu, Manyi Li +1 more
2/10/2026

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Abstract

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.

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@article{bai2026freescene,
  title  = {FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts},
  author = {Tongyuan Bai and Wangyuanfan Bai and Dong Chen and Tieru Wu and Manyi Li and Rui Ma},
  year   = {2026},
  eprint = {2506.02781},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2506.02781},
  journal = {CVPR 2025 2025}
}

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