mlsanityData to 14 Oct 2026

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

arXiv 2209.11163 ↗First posted 22 Sep 2022cs.CVcited by 446

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Cited by 142 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications. Prior works on 3D generative modeling either lack geometric details, are limited in the mesh topology they can produce, typically do not support textures, or utilize neural renderers in the synthesis process, which makes their use in common 3D software non-trivial. In this work, we introduce GET3D, a Generative model that directly generates Explicit Textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures. We bridge recent success in the differentiable surface modeling, differentiable rendering as well as 2D Generative Adversarial Networks to train our model from 2D image collections. GET3D is able to generate high-quality 3D textured meshes, ranging from cars, chairs, animals, motorbikes and human characters to buildings, achieving significant improvements over previous methods.

Key facts. Cited by 446 papers in the map (data through 14 Oct 2026). Builds on 55 of its 79 references that are in the map. First posted on arXiv 22 Sep 2022.

Builds on

55 of 79 references.

References · 55

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  6. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  7. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  8. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  9. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  10. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  11. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  12. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,205
38 more
  1. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  2. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  3. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 785
  4. 2020Convolutional Occupancy Networkscited by 761
  5. 2018Open3D: A Modern Library for 3D Data Processingcited by 721
  6. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  7. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
  8. 2018Which Training Methods for GANs do actually Converge?cited by 679
  9. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  10. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  11. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
  12. 2021StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesiscited by 477
  13. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 460
  14. 2020Modular Primitives for High-Performance Differentiable Renderingcited by 441
  15. 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 366
  16. 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 335
  17. 2021Text2Mesh: Text-Driven Neural Stylization for Meshescited by 334
  18. 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
  19. 2020PolyGen: An Autoregressive Generative Model of 3D Meshescited by 269
  20. 2021DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effortcited by 264
  21. 2019Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderercited by 221
  22. 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
  23. 2018Escaping Plato's Cave: 3D Shape From Adversarial Renderingcited by 170
  24. 2021CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesiscited by 149
  25. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  26. 2022VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Gridscited by 133
  27. 20163D Shape Induction from 2D Views of Multiple Objectscited by 121
  28. 20213D-aware Image Synthesis via Learning Structural and Textural Representationscited by 109
  29. 2020Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Renderingcited by 101
  30. 2021GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worldscited by 98
  31. 2020Learning Deformable Tetrahedral Meshes for 3D Reconstructioncited by 85
  32. 2021Multimodal Conditional Image Synthesis with Product-of-Experts GANscited by 75
  33. 2017Improved Adversarial Systems for 3D Object Generation and Reconstructioncited by 66
  34. 20213DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variationscited by 58
  35. 2020Convolutional Generation of Textured 3D Meshescited by 52
  36. 2021DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderercited by 52
  37. 2021Learning Generative Models of Textured 3D Meshes from Real-World Imagescited by 44
  38. 2021Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequencescited by 39

50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 23; 2023: 161; 2024: 128; 2025: 82; 2026: 52.

446 papers.

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  1. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,540
  2. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,489
  3. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,290
  4. 2023MVDream: Multi-view Diffusion for 3D Generationcited by 938
  5. 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 849
  6. 2023DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creationcited by 843
  7. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 770
  8. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 699
  9. 2023Wonder3D: Single Image to 3D using Cross-Domain Diffusioncited by 689
  10. 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Imagecited by 645
  11. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 560
  12. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
38 more
  1. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 458
  2. 2023Shap-E: Generating Conditional 3D Implicit Functionscited by 385
  3. 2023Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Modelcited by 378
  4. 2025Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generationcited by 356
  5. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 326
  6. 2023ReconFusion: 3D Reconstruction with Diffusion Priorscited by 318
  7. 2022Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusioncited by 309
  8. 2023TEXTure: Text-Guided Texturing of 3D Shapescited by 304
  9. 2024GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generationcited by 301
  10. 2023One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusioncited by 283
  11. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 259
  12. 2023Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformerscited by 258
  13. 2023ImageDream: Image-Prompt Multi-view Diffusion for 3D Generationcited by 257
  14. 2023GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Modelscited by 255
  15. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Modelscited by 235
  16. 2023LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenescited by 231
  17. 2024CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Modelcited by 215
  18. 2023DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Modelcited by 214
  19. 2022DiffRF: Rendering-Guided 3D Radiance Field Diffusioncited by 204
  20. 2023Scalable 3D Captioning with Pretrained Modelscited by 203
  21. 2022ECON: Explicit Clothed humans Optimized via Normal integrationcited by 199
  22. 2023MeshDiffusion: Score-based Generative 3D Mesh Modelingcited by 199
  23. 2024Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomographycited by 199
  24. 2023XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchiescited by 189
  25. 20233DGen: Triplane Latent Diffusion for Textured Mesh Generationcited by 183
  26. 2023Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstructioncited by 180
  27. 2023RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3Dcited by 168
  28. 2022RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generationcited by 162
  29. 2023DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Priorcited by 158
  30. 2023HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusioncited by 152
  31. 2022Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Modelscited by 151
  32. 2024MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformerscited by 144
  33. 2023Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fieldscited by 143
  34. 2023Flexible Isosurface Extraction for Gradient-Based Mesh Optimizationcited by 139
  35. 2024MeshLRM: Large Reconstruction Model for High-Quality Meshescited by 139
  36. 2022Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functionscited by 133
  37. 2023Locally Attentional SDF Diffusion for Controllable 3D Shape Generationcited by 130
  38. 2022Diffusion-SDF: Text-to-Shape via Voxelized Diffusioncited by 128

50 most cited of 446 All 446 (JSON)