HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes
arXiv:2606.06390v1 Announce Type: cross Abstract: Indoor scene generation is crucial for robot simulation and modern interior design. However, complex layouts together with scarce 3D scene data make learning-based generation challenging. Existing methods often rely on hand-crafted rules or focus on isolated sub-tasks (e.g., floorplan synthesis or single-room furnishing), producing whole-home scenes that lack global coherence, realism, and simulation readiness. To mitigate these limitations, we propose a unified hierarchical framework that decomposes indoor scene synthesis into controllable sta
The rapid advancement in AI, particularly in generative models and 3D reconstruction, is enabling more sophisticated and holistic approaches to virtual environment creation.
This development is crucial for training embodied AI, enhancing VR/AR experiences, and streamlining design processes across multiple industries, ultimately impacting the adoption of autonomous systems.
Indoor scene generation moves from siloed, rule-based systems to unified, AI-driven frameworks capable of producing coherent, interactive, and simulation-ready whole-home scenes.
- · Robotics simulation platforms
- · Interior design software companies
- · AI developers focused on embodied intelligence
- · VR/AR content creators
- · Traditional manual 3D asset creation firms
- · Companies relying on fragmented scene generation tools
More realistic and efficient training environments for robots will accelerate their development and deployment.
The ability to rapidly generate complex, interactive virtual spaces will drive innovation in architectural visualization and smart home planning.
This could lead to a 'metaverse' of highly realistic and functional digital twins for physical spaces, enabling new forms of remote collaboration and pre-visualization.
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Read at arXiv cs.AI