SpatialGrammar provides a grid-based DSL and compiler that lets LLMs generate collision-free 3D indoor scenes more reliably than raw-coordinate or code-based approaches.
Sceneweaver: All-in-one 3d scene synthesis with an extensible and self- reflective agent
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Architect-Ant fine-tunes a vision-language model on the new AntPlan-270 dataset using procedural reasoning traces and preference optimization to output editable DSL furniture layouts that can be rendered into images.
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.
STABLE generates simulation-ready tabletop scenes by alternating a semantic LLM reasoner for task-aligned coarse layouts with a physics corrector for physical plausibility using progressive scene expansion.
SceneCritic is a symbolic, ontology-grounded evaluator for floor-plan layouts that identifies specific semantic, orientation, and geometric violations and aligns better with human judgments than VLM-based scorers.
Pair2Scene generates complex 3D scenes beyond training data by training a network on local object-pair placement rules and applying them recursively with collision-aware sampling.
Code-as-Room is an MLLM-based agentic pipeline that parses top-down images into multi-stage Blender code synthesis with cross-stage memory to generate functional 3D rooms.
The paper surveys 3D asset generation methods and organizes them around the full production pipeline to assess which outputs meet engine-level requirements for interactive applications.
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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SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation
SpatialGrammar provides a grid-based DSL and compiler that lets LLMs generate collision-free 3D indoor scenes more reliably than raw-coordinate or code-based approaches.
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Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans
Architect-Ant fine-tunes a vision-language model on the new AntPlan-270 dataset using procedural reasoning traces and preference optimization to output editable DSL furniture layouts that can be rendered into images.
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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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STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics-Physics Dual System
STABLE generates simulation-ready tabletop scenes by alternating a semantic LLM reasoner for task-aligned coarse layouts with a physics corrector for physical plausibility using progressive scene expansion.
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SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis
SceneCritic is a symbolic, ontology-grounded evaluator for floor-plan layouts that identifies specific semantic, orientation, and geometric violations and aligns better with human judgments than VLM-based scorers.
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Pair2Scene: Learning Local Object Relations for Procedural Scene Generation
Pair2Scene generates complex 3D scenes beyond training data by training a network on local object-pair placement rules and applying them recursively with collision-aware sampling.
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Code-as-Room: Generating 3D Rooms from Top-Down View Images via Agentic Code Synthesis
Code-as-Room is an MLLM-based agentic pipeline that parses top-down images into multi-stage Blender code synthesis with cross-stage memory to generate functional 3D rooms.
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From Visual Synthesis to Interactive Worlds: Toward Production-Ready 3D Asset Generation
The paper surveys 3D asset generation methods and organizes them around the full production pipeline to assess which outputs meet engine-level requirements for interactive applications.
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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.