RoomCraft: Controllable and Complete 3D Indoor Scene Generation
Mengqi Zhou, Xipeng Wang, Yuxi Wang, Zhaoxiang Zhang

TL;DR
RoomCraft is a multi-stage pipeline that converts various user inputs into realistic, coherent 3D indoor scenes by combining scene understanding, constraint-driven optimization, and a conflict-aware placement strategy.
Contribution
It introduces a novel unified constraint representation and a conflict-aware positioning strategy to improve multi-constraint scene generation.
Findings
Outperforms existing methods in realism and coherence
Handles complex multi-constraint scenarios effectively
Produces visually appealing room layouts across diverse inputs
Abstract
Generating realistic 3D indoor scenes from user inputs remains a challenging problem in computer vision and graphics, requiring careful balance of geometric consistency, spatial relationships, and visual realism. While neural generation methods often produce repetitive elements due to limited global spatial reasoning, procedural approaches can leverage constraints for controllable generation but struggle with multi-constraint scenarios. When constraints become numerous, object collisions frequently occur, forcing the removal of furniture items and compromising layout completeness. To address these limitations, we propose RoomCraft, a multi-stage pipeline that converts real images, sketches, or text descriptions into coherent 3D indoor scenes. Our approach combines a scene generation pipeline with a constraint-driven optimization framework. The pipeline first extracts high-level scene…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
