3D Scene Diffusion Guidance using Scene Graphs
Not on the map. Why?
Abstract (arXiv)
Guided synthesis of high-quality 3D scenes is a challenging task. Diffusion models have shown promise in generating diverse data, including 3D scenes. However, current methods rely directly on text embeddings for controlling the generation, limiting the incorporation of complex spatial relationships between objects. We propose a novel approach for 3D scene diffusion guidance using scene graphs. To leverage the relative spatial information the scene graphs provide, we make use of relational graph convolutional blocks within our denoising network. We show that our approach significantly improves the alignment between scene description and generated scene.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 33 references that are in the map. First posted on arXiv 8 Aug 2023.
Builds on
27 of 33 references.
References · 27
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
- 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
- 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
15 more
- 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
- 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
- 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
- 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
- 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 449
- 20203D-FUTURE: 3D Furniture shape with TextUREcited by 306
- 2020Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructionscited by 205
- 2021ATISS: Autoregressive Transformers for Indoor Scene Synthesiscited by 195
- 2019RIO: 3D Object Instance Re-Localization in Changing Indoor Environmentscited by 187
- 2020SceneFormer: Indoor Scene Generation with Transformerscited by 154
- 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
- 2021Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidancecited by 84
- 2022HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoisingcited by 72
- 2021Indoor Scene Generation from a Collection of Semantic-Segmented Depth Imagescited by 27
- 2022Learning 3D Scene Priors with 2D Supervisioncited by 24
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2023GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs
- 2026Dependency-Aware Discrete Diffusion for Scene Graph Generation
- 2022Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
- 2023SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis
- 2023Scene Graph Conditioning in Latent Diffusion
- 2024LT3SD: Latent Trees for 3D Scene Diffusion
- 2025DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation
- 2024Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs