mlsanityData to 14 Oct 2026

DreamAnywhere: Object-Centric Panoramic 3D Scene Generation

arXiv 2506.20367 ↗First posted 25 Jun 2025cs.GR cs.CVcited by 2

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Abstract (arXiv)

Recent advances in text-to-3D scene generation have demonstrated significant potential to transform content creation across multiple industries. Although the research community has made impressive progress in addressing the challenges of this complex task, existing methods often generate environments that are only front-facing, lack visual fidelity, exhibit limited scene understanding, and are typically fine-tuned for either indoor or outdoor settings. In this work, we address these issues and propose DreamAnywhere, a modular system for the fast generation and prototyping of 3D scenes. Our system synthesizes a 360deg panoramic image from text, decomposes it into background and objects, constructs a complete 3D representation through hybrid inpainting, and lifts object masks to detailed 3D objects that are placed in the virtual environment. DreamAnywhere supports immersive navigation and intuitive object-level editing, making it ideal for scene exploration, visual mock-ups, and rapid prototyping -- all with minimal manual modeling. These features make our system particularly suitable for low-budget movie production, enabling quick iteration on scene layout and visual tone without the overhead of traditional 3D workflows. Our modular pipeline is highly customizable as it allows components to be replaced independently. Compared to current state-of-the-art text and image-based 3D scene generation approaches, DreamAnywhere shows significant improvements in coherence in novel view synthesis and achieves competitive image quality, demonstrating its effectiveness across diverse and challenging scenarios. A comprehensive user study demonstrates a clear preference for our method over existing approaches, validating both its technical robustness and practical usefulness.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 71 of its 89 references that are in the map. First posted on arXiv 25 Jun 2025.

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71 of 89 references.

References · 71

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38 more
  1. 2023Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levelscited by 503
  2. 2024InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Modelscited by 433
  3. 2024ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesiscited by 403
  4. 2025GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Controlcited by 341
  5. 2023Recognize Anything: A Strong Image Tagging Modelcited by 309
  6. 2023LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matchingcited by 290
  7. 2019Structured3D: A Large Photo-realistic Dataset for Structured 3D Modelingcited by 286
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  12. 2020Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Imagecited by 243
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  14. 2024A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasetscited by 216
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  16. 2024Taming 3DGS: High-Quality Radiance Fields with Limited Resourcescited by 204
  17. 2024WonderWorld: Interactive 3D Scene Generation from a Single Imagecited by 202
  18. 2023SceneScape: Text-Driven Consistent Scene Generationcited by 160
  19. 2023WonderJourney: Going from Anywhere to Everywherecited by 158
  20. 2023State of the Art on Diffusion Models for Visual Computingcited by 127
  21. 2023Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fieldscited by 123
  22. 2024MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generationcited by 117
  23. 2024RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusioncited by 109
  24. 2025CAST: Component-Aligned 3D Scene Reconstruction from an RGB Imagecited by 106
  25. 2022ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removalcited by 103
  26. 2025Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generationcited by 101
  27. 2024BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolationcited by 83
  28. 2024DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splattingcited by 82
  29. 2024DepthFM: Fast Monocular Depth Estimation with Flow Matchingcited by 76
  30. 2023Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenescited by 75
  31. 2023SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collectionscited by 72
  32. 2024DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Samplingcited by 72
  33. 2024pix2gestalt: Amodal Segmentation by Synthesizing Wholescited by 70
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  38. 2023ControlRoom3D: Room Generation using Semantic Proxy Roomscited by 56

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Citing papers per year: 2026: 2.

2 papers.