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https://github.com/justimyhxu/awesome-3D-generation

A curated list of awesome 3d generation papers
https://github.com/justimyhxu/awesome-3D-generation
List: awesome-3D-generation
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A curated list of awesome 3d generation papers
Host: GitHub
URL: https://github.com/justimyhxu/awesome-3D-generation
Owner: justimyhxu
Created: 2022-03-04T07:51:35.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2023-03-09T14:55:46.000Z (almost 2 years ago)
Last Synced: 2024-05-19T20:15:20.312Z (7 months ago)
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Watchers: 48
Forks: 52
Open Issues: 1
Metadata Files:
- Readme: README.md
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ultimate-awesome - awesome-3D-generation - A curated list of awesome 3d generation papers. (Other Lists / Monkey C Lists)
README

        # Awesome 3D Generation

*News*: We provide a survey, [*Deep Generative Models on 3D Representations: A Survey*](https://arxiv.org/abs/2210.15663), to help the community track the evolution of this field.

> **Deep Generative Models on 3D Representations: A Survey** 


> Zifan Shi*, Sida Peng*, Yinghao Xu*, Yiyi Liao, Yujun Shen


> *https://arxiv.org/abs/2210.15663*


> (* denotes equal contribution)

## Overview

This repository collects the studies on 3D generation, including both [3D shape generation](#3d-shape-generation) and [3D-aware image generation](#3d-aware-image-generation). Different from 3D reconstruction, which focuses on per-instance recovery (*i.e.*, the data already exists in the real world), 3D generation targets learning the real distribution and hence allows sampling new data.

Overall, the paper collection is organized as follows. *If you find some work is missing, feel free to raise an issue or create a pull request. We appreciate contributions in any form.*

- [3D Shape Generation](#3d-shape-generation)

  - [Point Cloud](#point-cloud)

  - [Voxel](#voxel)

  - [Mesh](#mesh)

  - [Neural Field](#neural-field)

  - [Program](#program)

- [3D-aware Image Generation](#3d-aware-image-generation)

  - [Voxel](#voxel-1)

  - [Depth](#depth)

  - [Neural Field](#neural-field-1)

  - [Hybrid Representation](#hybrid-representation)

- [3D Control of 2D Generative Models](#3d-control-of-2d-generative-models)

## 3D Shape Generation

We categorize the studies on 3D shape generation according to the representation used.

### Point Cloud

- Learning Representations and Generative Models for 3D Point Clouds 


  [ICML 2018](https://arxiv.org/abs/1707.02392) / [Code](https://github.com/optas/latent_3d_points)

- Multiresolution Tree Networks for 3D Point Cloud Processing 


  [ECCV 2018](https://openaccess.thecvf.com/content_ECCV_2018/papers/Matheus_Gadelha_Multiresolution_Tree_Networks_ECCV_2018_paper.pdf) / [Code](https://github.com/matheusgadelha/MRTNet) / [Project Page](http://mgadelha.me/mrt/)

- 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions 


  [ICCV 2019](https://arxiv.org/abs/1905.06292) / [Code](https://github.com/prajwalsingh/TreeGCN-GAN)

- Point Cloud GAN 


  [ICLR 2019](https://arxiv.org/abs/1810.05795) / [Code](https://github.com/chunliangli/Point-Cloud-GAN)

- Learning Localized Generative Models for 3D Point Clouds via Graph Convolution 


  [ICLR 2019](https://openreview.net/pdf?id=SJeXSo09FQ) / [Code](https://github.com/diegovalsesia/GraphCNN-GAN)

- PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows 


  [ICCV 2019](https://arxiv.org/abs/1906.12320) / [Code](https://github.com/stevenygd/PointFlow)

- Spectral-GANs for High-Resolution 3D Point-Cloud Generation 


  [IROS 2020](https://arxiv.org/abs/1912.01800) / [Code](https://github.com/samgregoost/Spectral-GAN)

- Progressive Point Cloud Deconvolution Generation Network 


  [ECCV 2020](https://arxiv.org/abs/2007.05361) / [Code](https://github.com/fpthink/PDGN)

- A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds 


  [3DV 2020](https://arxiv.org/abs/2010.05391) / [Code](https://github.com/robotic-vision-lab/Progressive-Conditional-Generative-Adversarial-Network)

- Adversarial Autoencoders for Generating 3D Point Clouds 


  [ICLR 2020](https://arxiv.org/abs/1811.07605) / [Code](https://github.com/MaciejZamorski/3d-AAE)

- Learning Gradient Fields for Shape Generation 


  [ECCV 2020](https://arxiv.org/abs/2008.06520) / [Code](https://github.com/RuojinCai/ShapeGF) / [Project Page](https://www.cs.cornell.edu/~ruojin/ShapeGF/)

- SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds 


  [NeurIPS 2020](https://arxiv.org/abs/2006.04604) / [Code](https://github.com/ANLGBOY/SoftFlow)

- Discrete Point Flow Networks for Efficient Point Cloud Generation 


  [ECCV 2020](https://arxiv.org/abs/2007.10170) / [Code](https://github.com/Regenerator/dpf-nets)

- Pointgrow: Autoregressively Learned Point Cloud Generation with Self-Attention 


  [WACV 2020](https://arxiv.org/abs/1810.05591) / [Code](https://github.com/syb7573330/PointGrow) / [Project Page](https://liuziwei7.github.io/projects/PointGrow)

- MRGAN: MultiRooted 3D Shape Generation with Unsupervised Part Disentanglement 


  [ICCVW 2021](https://arxiv.org/abs/2007.12944)

- SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation 


  [SIGGRAPH 2021](https://arxiv.org/abs/2108.04476) / [Code](https://github.com/liruihui/sp-gan)

- Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for

3D Generation, Reconstruction and Classification 


  [CVPR 2021](https://arxiv.org/abs/2004.01301) / [Code](https://github.com/fei960922/GPointNet) / [Project Page](http://www.stat.ucla.edu/~jxie/GPointNet/)

- Diffusion Probabilistic Models for 3D Point Cloud Generation 


  [CVPR 2021](https://arxiv.org/abs/2103.01458) / [Code](https://github.com/luost26/diffusion-point-cloud)

- 3D Shape Generation and Completion through Point-Voxel Diffusion 


  [ICCV 2021](https://arxiv.org/abs/2104.03670) / [Code](https://github.com/alexzhou907/PVD) / [Project Page](https://alexzhou907.github.io/pvd)

- ManiFlow: Implicitly Representing Manifolds with Normalizing Flows 


  [3DV 2022](https://arxiv.org/abs/2208.08932)

- LION: Latent Point Diffusion Models for 3D Shape Generation 


  [NeurIPS 2022](https://arxiv.org/abs/2210.06978) / [Code](https://github.com/nv-tlabs/LION) / [Project Page](https://nv-tlabs.github.io/LION/)

### Voxel

- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling 


  [NeurIPS 2016](https://arXiv.org/abs/1610.07584) / [Code](https://github.com/zck119/3dgan-release) / [Project Page](http://3dgan.csail.mit.edu/)

- Generative and Discriminative Voxel Modeling with Convolutional Neural Networks 


  [arXiv 2016](https://arxiv.org/abs/1608.04236) / [Code](https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling)

- SAGNet: Structure-aware Generative Network for 3D-Shape Modeling 


  [SIGGRAPH 2019](https://dilincv.github.io/papers/SAGNet_sig2019.pdf) / [Code](https://github.com/zhijieW94/SAGNet) / [Project Page](https://vcc.tech/research/2019/SAGnet/)

- Generalized Autoencoder for Volumetric Shape Generation 


  [CVPRW 2020](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w17/Guan_Generalized_Autoencoder_for_Volumetric_Shape_Generation_CVPRW_2020_paper.pdf) / [Code](https://github.com/IsaacGuan/3D-GAE)

- PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes 


  [CVPR 2020](https://arxiv.org/abs/1911.10949) / [Code](https://github.com/ChrisWu1997/PQ-NET)

- Learning Part Generation and Assembly for Structure-Aware Shape Synthesis 


  [AAAI 2020](https://arxiv.org/abs/1906.06693)

- Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis 


  [TPAMI 2020](http://www.stat.ucla.edu/~jxie/3DDescriptorNet/3DDescriptorNet_file/doc/3DDescriptorNet.pdf) / [Code](https://github.com/jianwen-xie/3DDescriptorNet) / [Project Page](http://www.stat.ucla.edu/~jxie/3DDescriptorNet/3DDescriptorNet.html)

- Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences 


  [arXiv 2021](https://arxiv.org/abs/2111.12480)

- AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation 


  [CVPR 2022](http://arxiv.org/abs/2203.09516) / [Project Page](https://yccyenchicheng.github.io/AutoSDF/) / [Code](https://github.com/yccyenchicheng/AutoSDF/)

### Mesh

- SDM-Net: Deep Generative Network for Structured Deformable Mesh 


  [SIGGRAPH Asia 2019](https://arxiv.org/abs/1908.04520) / [Project Page](http://geometrylearning.com/sdm-net/)

- PolyGen: An Autoregressive Generative Model of 3D Meshes 


  [ICML 2020](https://arxiv.org/abs/2002.10880) / [Code](https://github.com/deepmind/deepmind-research/tree/master/polygen)

- TM-NET: Deep Generative Networks for Textured Meshes 


  [TOG 2021](https://arxiv.org/abs/2010.06217) / [Code](https://github.com/IGLICT/TM-NET) / [Project Page](http://geometrylearning.com/TM-NET/)

- Multi-Chart Generative Surface Modeling 


  [SIGGRAPH Asia 2018](https://arxiv.org/abs/1806.02143) / [Code](https://github.com/helibenhamu/multichart3dgans)

### Neural Field

- Learning Implicit Fields for Generative Shape Modeling 


  [CVPR 2019](https://arxiv.org/abs/1812.02822) / [Code](https://github.com/czq142857/implicit-decoder) / [Project Page](https://www.sfu.ca/~zhiqinc/imgan/Readme.html)

- Adversarial Generation of Continuous Implicit Shape Representations 


  [EG 2020](https://arxiv.org/abs/2002.00349) / [Code](https://github.com/marian42/shapegan)

- DualSDF: Semantic Shape Manipulation using a Two-Level Representation 


  [CVPR 2020](https://arxiv.org/abs/2004.02869) / [Code](https://github.com/zekunhao1995/DualSDF) / [Project Page](https://www.cs.cornell.edu/~hadarelor/dualsdf/)

- Physically-Aware Generative Network for 3D Shape Modeling 


  [CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Mezghanni_Physically-Aware_Generative_Network_for_3D_Shape_Modeling_CVPR_2021_paper.html)

- SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators 


  [ICCV 2021](https://arxiv.org/abs/2201.00112)

- 3D Shape Generation with Grid-Based Implicit Functions 


  [CVPR 2021](https://arxiv.org/abs/2107.10607) / [Code](https://www.graphics.rwth-aachen.de/person/231/)

- Deformed Implicit Field: Modeling 3D shapes with Learned Dense Correspondence 


  [CVPR 2021](https://arxiv.org/abs/2011.13650) / [Code](https://github.com/microsoft/DIF-Net)

- gDNA: Towards Generative Detailed Neural Avatars 


  [CVPR 2022](https://arxiv.org/abs/2201.04123) / [Code](https://github.com/xuchen-ethz/gdna) / [Project Page](https://xuchen-ethz.github.io/gdna/)

- ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model 


  [arXiv 2022](https://arxiv.org/abs/2207.09446)

- Learning to Generate 3D Shapes from a Single Example 


  [arXiv 2022](https://arxiv.org/abs/2208.02946) / [Code](https://github.com/ChrisWu1997/SingleShapeGen) / [Project Page](http://www.cs.columbia.edu/cg/SingleShapeGen/)

- 3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models 


  [arXiv 2022](https://arxiv.org/abs/2212.00842)

### Program

- ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis 


  [SIGGRAPH Asia 2020](https://arxiv.org/abs/2009.08026) / [Code](https://github.com/rkjones4/ShapeAssembly) / [Project Page](https://rkjones4.github.io/shapeAssembly.html)

## 3D-aware Image Generation

We categorize the studies on 3D-aware image generation according to the representation used.

### Voxel

- Visual Object Networks: Image Generation with Disentangled 3D Representation 


  [NeurIPS 2018](https://arXiv.org/abs/1812.02725) / [Code](https://github.com/junyanz/VON) / [Project Page](http://von.csail.mit.edu/)

- HoloGAN: Unsupervised Learning of 3D representations from Natural Images 


  [ICCV 2019](https://arXiv.org/abs/1904.01326) / [Code](https://github.com/thunguyenphuoc/HoloGAN)

- BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images 


  [NeurIPS 2020](https://arXiv.org/abs/2002.08988) / [Code](https://github.com/thunguyenphuoc/BlockGAN)

- Towards a Neural Graphics Pipeline for Controllable Image Generation 


  [Computer Graphics Forum 2021](https://arXiv.org/abs/2006.10569) / [Project Page](http://geometry.cs.ucl.ac.uk/projects/2021/ngp/)

- VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids 


  [NeurIPS 2022](https://arxiv.org/abs/2206.07695)

### Depth

- Generative Image Modeling using Style and Structure Adversarial Networks 


  [ECCV 2016](https://arXiv.org/abs/1603.05631) / [Code](https://github.com/xiaolonw/ss-gan)

- RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis 


  [ICLR 2020](https://arXiv.org/abs/1909.12573) / [Code](https://github.com/nogu-atsu/RGBD-GAN)

- 3D-Aware Indoor Scene Synthesis with Depth Priors 


  [ECCV 2022](https://arXiv.org/abs/2202.08553) / [Code](https://github.com/VivianSZF/depthgan) / [Project Page](https://vivianszf.github.io/depthgan/)

### Neural Field

- GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis 


  [NeurIPS 2020](https://arXiv.org/abs/2007.02442) / [Code](https://github.com/autonomousvision/graf) / [Project Page](https://autonomousvision.github.io/graf/)

- NeRF-VAE: A Geometry Aware 3D Scene Generative Model 


  [ICML 2021](https://arxiv.org/abs/2104.00587)

- GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields 


  [CVPR 2021](https://arXiv.org/abs/2011.12100) / [Code](https://github.com/autonomousvision/giraffe) / [Project Page](https://m-niemeyer.github.io/project-pages/giraffe/index.html)

- pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis 


  [CVPR 2021](https://arXiv.org/abs/2012.00926) / [Code](https://github.com/marcoamonteiro/pi-GAN) / [Project Page](https://marcoamonteiro.github.io/pi-GAN-website/)

- Unconstrained Scene Generation with Locally Conditioned Radiance Fields 


  [ICCV 2021](https://arXiv.org/abs/2104.00670) / [Code](https://github.com/apple/ml-gsn) / [Project Page](https://apple.github.io/ml-gsn/)

- A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis 


  [NeurIPS 2021](https://arXiv.org/abs/2110.15678) / [Code](https://github.com/xingangpan/shadegan) / [Project Page](https://xingangpan.github.io/projects/ShadeGAN.html)

- Campari: Camera-aware Decomposed Generative Neural Radiance Fields 


  [3DV 2021](https://arXiv.org/abs/2103.17269) / [Code](https://github.com/autonomousvision/campari)

- CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis 


  [arXiv 2021](https://arXiv.org/abs/2110.09788) / [Code](https://github.com/PeterouZh/CIPS-3D)

- GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds 


  [ICCV 2021](https://arXiv.org/abs/2104.07659) / [Code](https://github.com/NVlabs/GANcraft) / [Project Page](https://nvlabs.github.io/GANcraft/)

- Generative Occupancy Fields for 3D Surface-Aware Image Synthesis 


  [NeurIPS 2021](https://arXiv.org/abs/2111.00969) / [Code](https://github.com/SheldonTsui/GOF_NeurIPS2021) / [Project Page](https://sheldontsui.github.io/projects/GOF)

- 3D-Aware Semantic-Guided Generative Model for Human Synthesis 


  [ECCV 2022](https://arXiv.org/abs/2112.01422) / [Code](https://github.com/zhangqianhui/3DSGAN) 

- FENeRF: Face Editing in Neural Radiance Fields 


  [CVPR 2022](https://arXiv.org/abs/2111.15490) / [Code](https://github.com/MrTornado24/FENeRF) / [Project Page](https://mrtornado24.github.io/FENeRF/)

- StyleNeRF: A Style-Based 3D-Aware Generator for High-resolution Image Synthesis 


  [ICLR 2022](https://arXiv.org/abs/2110.08985) / [Code](https://github.com/facebookresearch/StyleNeRF) / [Project Page](http://jiataogu.me/style_nerf/)

- StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation 


  [CVPR 2022](https://arXiv.org/abs/2112.11427) / [Code](https://github.com/royorel/StyleSDF) / [Project Page](https://stylesdf.github.io/)

- GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation 


  [CVPR 2022](https://arXiv.org/abs/2112.08867) / [Code](https://github.com/microsoft/GRAM) / [Project Page](https://yudeng.github.io/GRAM/)

- A Generative Model for 3D Face Synthesis with HDRI Relighting 


  [arXiv 2022](https://arxiv.org/abs/2201.04873) / [Code](https://github.com/google/volux-gan) / [Project Page](https://augmentedperception.github.io/voluxgan/)

- Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translation 


  [CVPR 2022](https://arXiv.org/abs/2202.13162) / [Code](https://github.com/primecai/Pix2NeRF)

- 3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations 


  [arXiv 2022](https://arxiv.org/abs/2203.06457)

- GIRAFFE HD: A High-Resolution 3D-aware Generative Model 


  [CVPR 2022](https://arxiv.org/abs/2203.14954) / [Code](https://github.com/AustinXY/GIRAFFEHD) / [Project Page](https://austinxy.github.io/project-pages/giraffehd/)

- Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis 


  [CVPR 2022](https://arxiv.org/abs/2204.06307) / [Code](https://github.com/Xuanmeng-Zhang/MVCGAN)

- Disentangled3D: Learning a 3D Generative Model with Disentangled Geometry and Appearance from Monocular Images 


  [CVPR 2022](https://arxiv.org/abs/2203.15926) / [Project Page](https://vcai.mpi-inf.mpg.de/projects/D3D/)

- Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations 


  [ECCV 2022](https://arxiv.org/abs/2204.08839) / [Code](https://github.com/nogu-atsu/ENARF-GAN) / [Project Page](https://nogu-atsu.github.io/ENARF-GAN/)

- IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis 


  [SIGGRAPH Asia 2022](https://arxiv.org/abs/2205.15517) / [Code](https://github.com/MrTornado24/IDE-3D) / [Project Page](https://mrtornado24.github.io/IDE-3D/)

- GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds 


  [arXiv 2022](https://arxiv.org/abs/2206.07255) / [Project Page](https://jeffreyxiang.github.io/GRAM-HD/)

- EpiGRAF: Rethinking training of 3D GANs 


  [NeurIPS 2022](https://arxiv.org/abs/2206.10535) / [Code](https://github.com/universome/epigraf) / [Project Page](https://universome.github.io/epigraf)

- AvatarGen: a 3D Generative Model for Animatable Human Avatars 


  [arXiv 2022](https://arxiv.org/abs/2208.00561) / [Code](https://github.com/jfzhang95/AvatarGen)

- cGOF: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields 


  [NeurIPS 2022](https://arxiv.org/abs/2206.08361) / [Code](https://github.com/KeqiangSun/cGOF) / [Project Page](https://keqiangsun.github.io/projects/cgof/)

- Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator 


  [NeurIPS 2022](https://arxiv.org/abs/2209.15637) / [Code](https://github.com/VivianSZF/geod) / [Project Page](https://vivianszf.github.io/geod)

- GAUDI: A Neural Architect for Immersive 3D Scene Generation 


  [NeurIPS 2022](https://arxiv.org/abs/2207.13751) / [Code](https://github.com/apple/ml-gaudi)

- DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis 


  [arXiv 2022](https://arxiv.org/abs/2212.11984) / [Code](https://github.com/snap-research/discoscene) / [Project Page](https://snap-research.github.io/discoscene/)

- Learning 3D-aware Image Synthesis with Unknown Pose Distribution 


  [arXiv 2023](https://arxiv.org/abs/2301.07702) / [Code](https://github.com/VivianSZF/pof3d) / [Project Page](https://vivianszf.github.io/pof3d/)

- Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars 


  [CVPR 2023](https://arxiv.org/abs/2211.11208) / [Code](https://github.com/MrTornado24/Next3D) / [Project Page](https://mrtornado24.github.io/Next3D/)

### Hybrid Representation

- 3D-aware Image Synthesis via Learning Structural and Textural Representations 


  [CVPR 2022](https://arXiv.org/abs/2112.10759) / [Code](https://github.com/genforce/volumegan) / [Project Page](https://genforce.github.io/volumegan/)

- Efficient Geometry-aware 3D Generative Adversarial Networks 


  [CVPR 2022](https://arXiv.org/abs/2112.07945) / [Code](https://github.com/NVlabs/eg3d) / [Project Page](https://matthew-a-chan.github.io/EG3D/)

- Generative Neural Articulated Radiance Fields 


  [NeurIPS 2022](https://arxiv.org/abs/2206.14314) / [Project Page](https://www.computationalimaging.org/publications/gnarf/)

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


  [NeurIPS 2022](https://nv-tlabs.github.io/GET3D/assets/paper.pdf) / [Code](https://github.com/nv-tlabs/GET3D) / [Project Page](https://nv-tlabs.github.io/GET3D/)

- DiffRF: Rendering-Guided 3D Radiance Field Diffusion 


  [arXiv 2022](https://arxiv.org/abs/2212.01206) / [Project Page](https://sirwyver.github.io/DiffRF/)

- RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation 


  [arXiv 2022](https://arxiv.org/abs/2211.09869) / [Code](https://github.com/Anciukevicius/RenderDiffusion)

- Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion 


  [arXiv 2022](https://arxiv.org/abs/2212.06135) / [Project Page](https://3d-avatar-diffusion.microsoft.com)

- 3D Neural Field Generation using Triplane Diffusion 


  [arXiv 2022](https://arxiv.org/abs/2211.16677) / [Code](https://github.com/JRyanShue/NFD) / [Project Page](https://jryanshue.com/nfd/index.html)

## 3D Control of 2D Generative Models

Besides explicitly learning a 3D generative model, there are also some attempts working on the 3D controllability of 2D models.

- Interpreting the latent space of gans for semantic face editing 


  [CVPR 2020](https://arxiv.org/abs/1907.10786) / [Code](https://github.com/genforce/interfacegan) / [Project Page](https://genforce.github.io/interfacegan/)

- StyleRig: Rigging StyleGAN for 3D Control over Portrait Images 


  [CVPR 2020](https://arxiv.org/abs/2004.00121) / [Project Page](https://vcai.mpi-inf.mpg.de/projects/StyleRig/)

- Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning 


  [CVPR 2020](https://arxiv.org/abs/2004.11660) / [Code](https://github.com/microsoft/DiscoFaceGAN)

- Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis 


  [CVPR 2020](https://avg.is.tuebingen.mpg.de/publications/liao2020cvpr) / [Code](https://github.com/autonomousvision/controllable_image_synthesis)

- DiscoFaceGAN: Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning 


  [CVPR 2020](https://arxiv.org/pdf/2004.11660.pdf) / [Code](https://github.com/microsoft/DiscoFaceGAN)

- FreeStyleGAN: Free-view Editable Portrait Rendering with the Camera Manifold 


  [SIGGRAPH Asia 2021](https://arxiv.org/abs/2109.09378) / [Code](https://gitlab.inria.fr/fungraph/freestylegan) / [Project Page](https://repo-sam.inria.fr/fungraph/freestylegan/)

- Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs 


  [ICLR 2021](https://arxiv.org/abs/2011.00844) / [Code](https://github.com/XingangPan/GAN2Shape) / [Project Page](https://xingangpan.github.io/projects/GAN2Shape.html)

- Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis 


  [IJCV 2021](https://arxiv.org/abs/1911.09267) / [Code](https://github.com/genforce/higan) / [Project Page](https://genforce.github.io/higan/)

- Cross-Domain and Disentangled Face Manipulation with 3D Guidance 


  [TVCG 2021](https://arxiv.org/abs/2104.11228) / [Code](https://github.com/cassiePython/cddfm3d) / [Project Page](https://cassiepython.github.io/cddfm3d/index)

- Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering 


  [ICLR 2021](https://arxiv.org/abs/2010.09125) / [Project Page](https://nv-tlabs.github.io/GANverse3D/)

- Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN 


  [TOG 2021](https://arxiv.org/abs/2109.06166) / [Code](https://github.com/BadourAlBahar/pose-with-style) / [Project Page](https://pose-with-style.github.io/)

- SofGAN: A Portrait Image Generator with Dynamic Styling 


  [TOG 2021](https://arxiv.org/abs/2007.03780) / [Code](https://github.com/apchenstu/sofgan) / [Project Page](https://apchenstu.github.io/sofgan/)

- MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation 


  [AAAI 2022](https://arxiv.org/abs/2111.01048)

- HeadNeRF: A Real-time NeRF-based Parametric Head Model 


  [CVPR 2022](https://arxiv.org/abs/2112.05637) / [Code](https://github.com/CrisHY1995/headnerf) / [Project Page](https://hy1995.top/HeadNeRF-Project/)

- GAN2X: Non-Lambertian Inverse Rendering of Image GANs 


  [3DV 2022](https://arxiv.org/abs/2206.09244) / [Project Page](https://people.mpi-inf.mpg.de/~xpan/GAN2X/)

- DreamFusion: Text-to-3D using 2D Diffusion 


  [arXiv 2022](https://arxiv.org/abs/2209.14988) / [Project Page](https://dreamfusion3d.github.io)

- Magic3D: High-Resolution Text-to-3D Content Creation 


  [arXiv 2022](https://arxiv.org/abs/2211.10440) / [Project Page](https://deepimagination.cc/Magic3D/)

- 3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation 


  [AAAI 2023](https://arxiv.org/abs/2212.01103)

- Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation 


  [arXiv 2022](https://arxiv.org/abs/2212.00774) / [Code](https://github.com/pals-ttic/sjc/) / [Project Page](https://pals.ttic.edu/p/score-jacobian-chaining)