HSM: Hierarchical Scene Motifs for
Multi-Scale Indoor Scene Generation

1Simon Fraser University, 2Alberta Machine Intelligence Institute (Amii)
3DV 2025 Nectar Exploration Edge Track

Despite advances in indoor 3D scene layout generation, synthesizing scenes with dense object arrangements remains challenging. Existing methods primarily focus on large furniture while neglecting smaller objects, resulting in unrealistically empty scenes. Those that place small objects typically do not honor arrangement specifications, resulting in largely random placement not following the text description.

We present HSM, a hierarchical framework for indoor scene generation with dense object arrangements across spatial scales. Indoor scenes are inherently hierarchical, with surfaces supporting objects at different scales, from large furniture on floors to smaller objects on tables and shelves. HSM embraces this hierarchy and exploits recurring cross-scale spatial patterns to generate complex and realistic indoor scenes in a unified manner. Our experiments show that HSM outperforms existing methods by generating scenes that better conform to user input across room types and spatial configurations.

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BibTeX


      @article{pun2025hsm,
          title = {{HSM}: Hierarchical Scene Motifs for Multi-Scale Indoor Scene Generation},
          author = {Pun, Hou In Derek and Tam, Hou In Ivan and and Wang, Austin T. and Huo, Xiaoliang and Chang, Angel X. and Savva, Manolis},
          year = {2025},
          eprint = {2503.16848},
          archivePrefix = {arXiv}
      }
      

Acknowledgements

This work was funded in part by a CIFAR AI Chair, a Canada Research Chair, NSERC Discovery Grants, and enabled by support from the Digital Research Alliance of Canada. We also thank Jiayi Liu, Weikun Peng, and Qirui Wu for helpful discussions.