A hierarchical pipeline generates controllable whole-home 3D scenes from floorplans via LLMs, image models, and VLMs, releasing 300K floorplans and 5K scenes for embodied AI use.
Chord: Generation of collision-free, house-scale, and organized digital twins for 3d indoor scenes with controllable floor plans and optimal layouts
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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baseline 1representative citing papers
PhyMix unifies a new multi-aspect physics evaluator with implicit policy optimization and explicit test-time correction to produce single-image 3D indoor scenes that are both visually faithful and physically plausible.
DGAD disentangles geometry editing via semantic embeddings from appearance preservation via cross-attention retrieval inside diffusion models for object composition.
SPG-Layout combines statistical object priors with hierarchical large-object-first placement to produce physically plausible text-driven 3D scenes in non-Manhattan rooms and outperforms baselines on a new 500-scene benchmark.
citing papers explorer
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HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes
A hierarchical pipeline generates controllable whole-home 3D scenes from floorplans via LLMs, image models, and VLMs, releasing 300K floorplans and 5K scenes for embodied AI use.
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PhyMix: Towards Physically Consistent Single-Image 3D Indoor Scene Generation with Implicit--Explicit Optimization
PhyMix unifies a new multi-aspect physics evaluator with implicit policy optimization and explicit test-time correction to produce single-image 3D indoor scenes that are both visually faithful and physically plausible.
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Geometry-Editable and Appearance-Preserving Object Compositon
DGAD disentangles geometry editing via semantic embeddings from appearance preservation via cross-attention retrieval inside diffusion models for object composition.
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Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments
SPG-Layout combines statistical object priors with hierarchical large-object-first placement to produce physically plausible text-driven 3D scenes in non-Manhattan rooms and outperforms baselines on a new 500-scene benchmark.