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Awesome 3D Generation

Overview

This repository collects the studies on 3D generation, including both 3D shape generation and 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

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 / Code
  • Multiresolution Tree Networks for 3D Point Cloud Processing
    ECCV 2018 / Code / Project Page
  • 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
    ICCV 2019 / Code
  • Point Cloud GAN
    ICLR 2019 / Code
  • Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
    ICLR 2019 / Code
  • PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows
    ICCV 2019 / Code
  • Spectral-GANs for High-Resolution 3D Point-Cloud Generation
    IROS 2020 / Code
  • Progressive Point Cloud Deconvolution Generation Network
    ECCV 2020 / Code
  • A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds
    3DV 2020 / Code
  • SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation
    SIGGRAPH 2021 / Code
  • Adversarial Autoencoders for Generating 3D Point Clouds
    ICLR 2020 / Code
  • Learning Gradient Fields for Shape Generation
    ECCV 2020 / Code / Project Page
  • SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
    NeurIPS 2020 / Code
  • Discrete Point Flow Networks for Efficient Point Cloud Generation
    ECCV 2020 / Code
  • Pointgrow: Autoregressively Learned Point Cloud Generation with Self-Attention
    WACV 2020 / Code / Project Page
  • MRGAN: MultiRooted 3D Shape Generation with Unsupervised Part Disentanglement
    ICCVW 2021
  • Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification
    CVPR 2021 / Code / Project Page

Voxel

  • 3d Shapenets: A Deep Representation for Volumetric Shapes
    CVPR 2015 / Code / Project Page
  • Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
    NeurIPS 2016 / Code / Project Page
  • Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
    arXiv 2016 / Code
  • Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs
    ICCV 2017 / Code
  • SAGNet: Structure-aware Generative Network for 3D-Shape Modeling
    SIGGRAPH 2019 / Code / Project Page
  • Generalized Autoencoder for Volumetric Shape Generation
    CVPRW 2020 / Code
  • PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes
    CVPR 2020 / Code
  • Learning Part Generation and Assembly for Structure-Aware Shape Synthesis
    AAAI 2020
  • Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
    TPAMI 2020 / Code / Project Page
  • DLGAN: Depth-Preserving Latent Generative Adversarial Network for 3D Reconstruction
    TMM 2020
  • DECOR-GAN: 3D Shape Detailization by Conditional Refinement
    CVPR 2021 / Code
  • Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences
    arXiv 2021

Mesh

Implicit Function

  • Learning Implicit Fields for Generative Shape Modeling
    CVPR 2019 / Code / Project Page
  • Adversarial Generation of Continuous Implicit Shape Representations
    arXiv 2020 / Code
  • DualSDF: Semantic Shape Manipulation using a Two-Level Representation
    CVPR 2020 / Code / Project Page
  • SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators
    ICCV 2021
  • 3D Shape Generation with Grid-Based Implicit Functions
    CVPR 2021
  • gDNA: Towards Generative Detailed Neural Avatars
    CVPR 2022 / Code / Project Page
  • Deformed Implicit Field: Modeling 3D shapes with Learned Dense Correspondence
    CVPR 2021 / Code

Parametric Surface

Primitive Shape

  • Physically-Aware Generative Network for 3D Shape Modeling
    CVPR 2021

Hybrid Representation

  • Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
    ECCV 2020
  • Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
    NeurIPS 2021 / Project Page

Program

3D-aware Image Generation

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

Point Cloud

  • Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks
    arXiv 2019

Voxel

Depth

  • Generative Image Modeling using Style and Structure Adversarial Networks
    ECCV 2016 / Code
  • Geometric Image Synthesis
    ACCV 2018
  • RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis
    ICLR 2020 / Code
  • 3D-Aware Indoor Scene Synthesis with Depth Priors
    arXiv 2022 / Code / Project Page

Implicit Function

  • GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
    NeurIPS 2020 / Code / Project Page
  • GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
    CVPR 2021 / Code / Project Page
  • pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
    CVPR 2021 / Code / Project Page
  • Unconstrained Scene Generation with Locally Conditioned Radiance Fields
    ICCV 2021 / Code / Project Page
  • A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
    NeurIPS 2021 / Code / Project Page
  • Campari: Camera-aware Decomposed Generative Neural Radiance Fields
    3DV 2021 / Code
  • CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
    arXiv 2021 / Code
  • GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds
    ICCV 2021 / Code / Project Page
  • Generative Occupancy Fields for 3D Surface-Aware Image Synthesis
    NeurIPS 2021 / Code / Project Page
  • 3D-Aware Semantic-Guided Generative Model for Human Synthesis
    arXiv 2021
  • FENeRF: Face Editing in Neural Radiance Fields
    CVPR 2022
  • StyleNeRF: A Style-Based 3D-Aware Generator for High-resolution Image Synthesis
    ICLR 2022 / Code / Project Page
  • StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation
    CVPR 2022 / Code / Project Page
  • GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation
    CVPR 2022 / Project Page
  • A Generative Model for 3D Face Synthesis with HDRI Relighting
    arXiv 2022
  • Pix2NeRF: Unsupervised Conditional ฯ€-GAN for Single Image to Neural Radiance Fields Translation
    CVPR 2022
  • 3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations
    arXiv 2022

Hybrid Representation

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.

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