mlsanityData to 7 Oct 2026

Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects

arXiv 2401.13203 ↗First posted 24 Jan 2024cs.CVcited by 1

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Abstract (arXiv)

Controllable 3D indoor scene synthesis stands at the forefront of technological progress, offering various applications like gaming, film, and augmented/virtual reality. The capability to stylize and de-couple objects within these scenarios is a crucial factor, providing an advanced level of control throughout the editing process. This control extends not just to manipulating geometric attributes like translation and scaling but also includes managing appearances, such as stylization. Current methods for scene stylization are limited to applying styles to the entire scene, without the ability to separate and customize individual objects. Addressing the intricacies of this challenge, we introduce a unique pipeline designed for synthesis 3D indoor scenes. Our approach involves strategically placing objects within the scene, utilizing information from professionally designed bounding boxes. Significantly, our pipeline prioritizes maintaining style consistency across multiple objects within the scene, ensuring a cohesive and visually appealing result aligned with the desired aesthetic. The core strength of our pipeline lies in its ability to generate 3D scenes that are not only visually impressive but also exhibit features like photorealism, multi-view consistency, and diversity. These scenes are crafted in response to various natural language prompts, demonstrating the versatility and adaptability of our model.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 32 references that are in the map. First posted on arXiv 24 Jan 2024.

Builds on

27 of 32 references.

References · 27

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2020Denoising Diffusion Implicit Modelscited by 9,655
  6. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,207
  7. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  8. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  9. 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Imagecited by 638
  10. 2023Zero123++: a Single Image to Consistent Multi-view Diffusion Base Modelcited by 516
  11. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  12. 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 375
15 more
  1. 2022SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generationcited by 287
  2. 2023Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Modelscited by 245
  3. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Modelscited by 234
  4. 2023MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusioncited by 210
  5. 2023MeshDiffusion: Score-based Generative 3D Mesh Modelingcited by 198
  6. 2023LEGO-Net: Learning Regular Rearrangements of Objects in Roomscited by 75
  7. 2023Texture Generation on 3D Meshes with Point-UV Diffusioncited by 63
  8. 2022DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesiscited by 61
  9. 2023Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraintscited by 59
  10. 2021StyleMesh: Style Transfer for Indoor 3D Scene Reconstructionscited by 58
  11. 2023CC3D: Layout-Conditioned Generation of Compositional 3D Scenescited by 58
  12. 2023RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texturecited by 53
  13. 2023SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priorscited by 43
  14. 2023DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagationcited by 25
  15. 2023Text2Scene: Text-driven Indoor Scene Stylization with Part-aware Detailscited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.