# DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagation

arXiv: https://arxiv.org/abs/2310.13119 · HTML: https://mlsanity.com/paper/2310.13119/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 61 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 30 of 46)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://mlsanity.com/paper/2211.07600/) (2022)
- [Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions](https://mlsanity.com/paper/2303.12789/) (2023)
- [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021)
- [MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction](https://mlsanity.com/paper/2206.00665/) (2022)
- [GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://mlsanity.com/paper/2209.11163/) (2022)
- [Modular Primitives for High-Performance Differentiable Rendering](https://mlsanity.com/paper/2011.03277/) (2020)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023)
- [CLIP-Mesh: Generating textured meshes from text using pretrained image-text models](https://mlsanity.com/paper/2203.13333/) (2022)
- [Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models](https://mlsanity.com/paper/2303.11989/) (2023)
- [Text2Tex: Text-driven Texture Synthesis via Diffusion Models](https://mlsanity.com/paper/2303.11396/) (2023)
- [MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion](https://mlsanity.com/paper/2307.01097/) (2023)

## Built on by (most cited first, 25 of 25)

- [Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models](https://mlsanity.com/paper/2312.13913/) (2023)
- [4K4DGen: Panoramic 4D Generation at 4K Resolution](https://mlsanity.com/paper/2406.13527/) (2024)
- [DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models](https://mlsanity.com/paper/2405.17176/) (2024)
- [DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion](https://mlsanity.com/paper/2410.24203/) (2024)
- [CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation](https://mlsanity.com/paper/2501.17162/) (2025)
- [DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training](https://mlsanity.com/paper/2510.11712/) (2025)
- [HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation](https://mlsanity.com/paper/2504.13072/) (2025)
- [WorldGrow: Generating Infinite 3D World](https://mlsanity.com/paper/2510.21682/) (2025)
- [RoomTex: Texturing Compositional Indoor Scenes via Iterative Inpainting](https://mlsanity.com/paper/2406.02461/) (2024)
- [What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?](https://mlsanity.com/paper/2505.22129/) (2025)
- [Taming Stable Diffusion for Text to 360deg Panorama Image Generation](https://mlsanity.com/paper/2404.07949/) (2024)
- [DreamCube: 3D Panorama Generation via Multi-plane Synchronization](https://mlsanity.com/paper/2506.17206/) (2025)
- [RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing](https://mlsanity.com/paper/2412.16778/) (2024)
- [Advances in 3D Neural Stylization: A Survey](https://mlsanity.com/paper/2311.18328/) (2023)
- [PanoWorld-X: Generating Explorable Panoramic Worlds via Sphere-Aware Video Diffusion](https://mlsanity.com/paper/2509.24997/) (2025)
- [M3DLayout: A Multi-Source Dataset of 3D Indoor Layouts and Structured Descriptions for 3D Generation](https://mlsanity.com/paper/2509.23728/) (2025)
- [3D StreetUnveiler with Semantic-aware 2DGS -- a simple baseline](https://mlsanity.com/paper/2405.18416/) (2024)
- [StyleCity: Large-Scale 3D Urban Scenes Stylization](https://mlsanity.com/paper/2404.10681/) (2024)
- [Prim2Room: Layout-Controllable Room Mesh Generation from Primitives](https://mlsanity.com/paper/2409.05380/) (2024)
- [Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects](https://mlsanity.com/paper/2401.13203/) (2024)
- [A Survey on Text-Driven 360-Degree Panorama Generation](https://mlsanity.com/paper/2502.14799/) (2025)
- [JoPano: Unified Panorama Generation via Joint Modeling](https://mlsanity.com/paper/2512.06885/) (2025)
- [Enhancing In-context Panoramic Generation via Geometric-aware Pretraining](https://mlsanity.com/paper/2607.08765/) (2026)
- [StreetDiff: Multi-view Street Scenes Generation via Cross-view Consistent Multi-view Stable Diffusion with Structure Prompts](https://mlsanity.com/paper/2609.09890/) (2026)
- [SceneHI: High-Resolution 3D-Consistent Scene Texturing with Controllable Illumination](https://mlsanity.com/paper/2609.10363/) (2026)

## Abstract (arXiv)

Diffusion-based methods have achieved prominent success in generating 2D media. However, accomplishing similar proficiencies for scene-level mesh texturing in 3D spatial applications, e.g., XR/VR, remains constrained, primarily due to the intricate nature of 3D geometry and the necessity for immersive free-viewpoint rendering. In this paper, we propose a novel indoor scene texturing framework, which delivers text-driven texture generation with enchanting details and authentic spatial coherence. The key insight is to first imagine a stylized 360deg panoramic texture from the central viewpoint of the scene, and then propagate it to the rest areas with inpainting and imitating techniques. To ensure meaningful and aligned textures to the scene, we develop a novel coarse-to-fine panoramic texture generation approach with dual texture alignment, which both considers the geometry and texture cues of the captured scenes. To survive from cluttered geometries during texture propagation, we design a separated strategy, which conducts texture inpainting in confidential regions and then learns an implicit imitating network to synthesize textures in occluded and tiny structural areas. Extensive experiments and the immersive VR application on real-world indoor scenes demonstrate the high quality of the generated textures and the engaging experience on VR headsets. Project webpage: https://ybbbbt.com/publication/dreamspace