Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models
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Cited by 67 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input. To this end, we leverage pre-trained 2D text-to-image models to synthesize a sequence of images from different poses. In order to lift these outputs into a consistent 3D scene representation, we combine monocular depth estimation with a text-conditioned inpainting model. The core idea of our approach is a tailored viewpoint selection such that the content of each image can be fused into a seamless, textured 3D mesh. More specifically, we propose a continuous alignment strategy that iteratively fuses scene frames with the existing geometry to create a seamless mesh. Unlike existing works that focus on generating single objects or zoom-out trajectories from text, our method generates complete 3D scenes with multiple objects and explicit 3D geometry. We evaluate our approach using qualitative and quantitative metrics, demonstrating it as the first method to generate room-scale 3D geometry with compelling textures from only text as input.
Key facts. Cited by 245 papers in the map (data through 7 Oct 2026). Builds on 81 of its 98 references that are in the map. First posted on arXiv 21 Mar 2023.
Builds on
81 of 98 references.
References · 81
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- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
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- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
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- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
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- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
- 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
- 2016Improved Techniques for Training GANscited by 2,938
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
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- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
- 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,544
- 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
- 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
- 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,285
- 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
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- 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 781
- 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
- 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
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- 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
- 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
- 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 475
- 2021CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fieldscited by 369
- 2019SynSin: End-to-end View Synthesis from a Single Imagecited by 364
- 2021Text2Mesh: Text-Driven Neural Stylization for Meshescited by 332
- 2023RealFusion: 360deg Reconstruction of Any Object from a Single Imagecited by 305
- 2023TEXTure: Text-Guided Texturing of 3D Shapescited by 303
- 2022Novel View Synthesis with Diffusion Modelscited by 299
- 2022SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generationcited by 287
- 2022CLIP-Mesh: Generating textured meshes from text using pretrained image-text modelscited by 281
50 most cited of 81 All 81 (JSON)
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Citing papers per year: 2023: 40; 2024: 87; 2025: 72; 2026: 46.
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