mlsanityData to 14 Oct 2026

Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches

arXiv 2408.04567 ↗First posted 8 Aug 2024cs.CV cs.GRcited by 21

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Abstract (arXiv)

3D Content Generation is at the heart of many computer graphics applications, including video gaming, film-making, virtual and augmented reality, etc. This paper proposes a novel deep-learning based approach for automatically generating interactive and playable 3D game scenes, all from the user's casual prompts such as a hand-drawn sketch. Sketch-based input offers a natural, and convenient way to convey the user's design intention in the content creation process. To circumvent the data-deficient challenge in learning (i.e. the lack of large training data of 3D scenes), our method leverages a pre-trained 2D denoising diffusion model to generate a 2D image of the scene as the conceptual guidance. In this process, we adopt the isometric projection mode to factor out unknown camera poses while obtaining the scene layout. From the generated isometric image, we use a pre-trained image understanding method to segment the image into meaningful parts, such as off-ground objects, trees, and buildings, and extract the 2D scene layout. These segments and layouts are subsequently fed into a procedural content generation (PCG) engine, such as a 3D video game engine like Unity or Unreal, to create the 3D scene. The resulting 3D scene can be seamlessly integrated into a game development environment and is readily playable. Extensive tests demonstrate that our method can efficiently generate high-quality and interactive 3D game scenes with layouts that closely follow the user's intention.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 39 of its 45 references that are in the map. First posted on arXiv 8 Aug 2024.

Builds on

39 of 45 references.

References · 39

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  5. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  6. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
  7. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  8. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  9. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  10. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,876
  11. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
  12. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
27 more
  1. 2024Grounded SAM: Assembling Open-World Models for Diverse Visual Taskscited by 929
  2. 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 849
  3. 2023Objaverse-XL: A Universe of 10M+ 3D Objectscited by 774
  4. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 738
  5. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 460
  6. 2023Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Modelscited by 245
  7. 2022Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpaintingcited by 233
  8. 2024CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Modelcited by 215
  9. 2023SceneScape: Text-Driven Consistent Scene Generationcited by 160
  10. 2023Osprey: Pixel Understanding with Visual Instruction Tuningcited by 159
  11. 2023WonderJourney: Going from Anywhere to Everywherecited by 158
  12. 2023Infinite Photorealistic Worlds using Procedural Generationcited by 147
  13. 2024MeshLRM: Large Reconstruction Model for High-Quality Meshescited by 139
  14. 2024Transparent Image Layer Diffusion using Latent Transparencycited by 114
  15. 2024RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusioncited by 109
  16. 2023Compositional 3D Scene Generation using Locally Conditioned Diffusioncited by 101
  17. 20233D-GPT: Procedural 3D Modeling with Large Language Modelscited by 95
  18. 2023Monocular Depth Estimation using Diffusion Modelscited by 87
  19. 2024BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolationcited by 83
  20. 2023ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusioncited by 65
  21. 2023Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraintscited by 60
  22. 2023ControlRoom3D: Room Generation using Semantic Proxy Roomscited by 56
  23. 2024TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusioncited by 52
  24. 2024Disentangled 3D Scene Generation with Layout Learningcited by 33
  25. 2024Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Planecited by 26
  26. 2023CityGen: Infinite and Controllable City Layout Generationcited by 22
  27. 2024Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesiscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 9; 2026: 9.

21 papers.

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  1. 2025MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous Drivingcited by 18
  2. 2025Sat2City: 3D City Generation from A Single Satellite Image with Cascaded Latent Diffusioncited by 16
  3. 2026AutoUE: Automated Generation of 3D Games in Unreal Engine via Multi-Agent Systemscited by 9
  4. 2026WorldClaw: Agentic 3D Open-World Generation at Scalecited by 8
  5. 2025MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layoutscited by 6
  6. 2026SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generationcited by 6
  7. 2025ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxiescited by 5
  8. 2025Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularizationcited by 3
  9. 2026Deep Sketch-Based 3D Modeling: A Surveycited by 3
  10. 2026Sat2City v2: Native 3D City Asset Generation from a Single Satellite Imagecited by 3
  11. 2024T3-S2S: Training-free Triplet Tuning for Sketch to Scene Synthesis in Controllable Concept Art Generationcited by 2
  12. 2025Edit3r: Instant 3D Scene Editing from Sparse Unposed Imagescited by 2
9 more
  1. 2024MS2Mesh-XR: Multi-modal Sketch-to-Mesh Generation in XR Environmentscited by 1
  2. 2025A Database-Driven Framework for 3D Level Generation with LLMscited by 1
  3. 2024Scene Co-pilot: Procedural Text to Video Generation with Human in the Loopcited by 0
  4. 2025GeoTexBuild: 3D Building Model Generation from Map Footprintscited by 0
  5. 2025Learning High-Quality Initial Noise for Single-View Synthesis with Diffusion Modelscited by 0
  6. 2026FLAT: Feedforward Latent Triangle Splatting for Geometrically Accurate Scene Generationcited by 0
  7. 2026BTC3D: Blended Tile Conditioning for Detail-Enhancing Image-to-3D Generationcited by 0
  8. 2026DiDE:Direct Injection with Color-Texture DEcoupling for 3D Stylizationcited by 0
  9. 2026Position Forcing: Self-Conditioning 3D Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Sketch-Guided Scene Image Generation
  2. 2025Recent Advances in 3D Object and Scene Generation: A Survey
  3. 2023NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
  4. 2024Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
  5. 2025In the Blink of an Eye: Instant Game Map Editing using a Generative-AI Smart Brush
  6. 20253D Scene Generation: A Survey
  7. 2024Training-Free Sketch-Guided Diffusion with Latent Optimization
  8. 2023Control3D: Towards Controllable Text-to-3D Generation