InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
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Abstract (arXiv)
Comprehending natural language instructions is a charming property for 3D indoor scene synthesis systems. Existing methods directly model object joint distributions and express object relations implicitly within a scene, thereby hindering the controllability of generation. We introduce InstructScene, a novel generative framework that integrates a semantic graph prior and a layout decoder to improve controllability and fidelity for 3D scene synthesis. The proposed semantic graph prior jointly learns scene appearances and layout distributions, exhibiting versatility across various downstream tasks in a zero-shot manner. To facilitate the benchmarking for text-driven 3D scene synthesis, we curate a high-quality dataset of scene-instruction pairs with large language and multimodal models. Extensive experimental results reveal that the proposed method surpasses existing state-of-the-art approaches by a large margin. Thorough ablation studies confirm the efficacy of crucial design components. Project page: https://chenguolin.github.io/projects/InstructScene.
Key facts. Cited by 110 papers in the map (data through 7 Oct 2026). Builds on 63 of its 83 references that are in the map. First posted on arXiv 7 Feb 2024.
Builds on
63 of 83 references.
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- 2020SceneFormer: Indoor Scene Generation with Transformerscited by 154
- 2018Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Modelscited by 105
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- 2023Autoregressive Diffusion Model for Graph Generationcited by 82
- 2023Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenescited by 75
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50 most cited of 63 All 63 (JSON)
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Citing papers per year: 2024: 17; 2025: 39; 2026: 54.
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50 newest of 110 All 110 (JSON)
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- 2024DeBaRA: Denoising-Based 3D Room Arrangement Generationcited by 20
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- 2025Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Modelscited by 7
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- 2026MUSE: Agentic 3D Scene Authoring via Memory-Grounded Incremental Requirement Satisfaction12 Jun 2026
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- 2025Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning5 Jun 2025
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- 2025SceneEval: Evaluating Semantic Coherence in Text-Conditioned 3D Indoor Scene Synthesiscited by 26
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- 2026Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation30 Sep 2026
- 2026Scene Retargeting: Learning Object Placement with Analogical Transfer29 Sep 2026
- 2026ESG: Generating Physically Consistent Dynamic 3D Scenes from Text Descriptions14 Sep 2026
- 2026FuncRoom-Agent: Sequential Feed-Forward 3D Functional Indoor Scene Generation30 Aug 2026
- 2026Roomer: Reflective Object-Grounded Model Editing and Repair for 3D Indoor Layout Synthesis3 Aug 2026
- 2026SG-Layout: Structured Scene Graph-Guided Layout Generation with LLMs2 Aug 2026
- 2026Learning an Interior Layout Policy in a Domain Specific Language Action Space31 Jul 2026
- 2026PlanCraft: Sketch, Refine, and Furnish for Architect-Inspired Progressive 3D Residential Scene Generation26 Jul 2026
- 2026CR-Refiner: An Object-Centric Optimal Transport Reranker for Edit-Conditioned 3D Scene Retrieval21 Jul 2026
- 2026Annotation-Free Furniture Codes: What They Encode, and How Far They Transfer11 Jul 2026
- 2026Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification6 Jul 2026
- 2026Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments2 Jul 2026
12 newest of 75 All 75 (JSON)