A curated list of awesome AIGC 3D papers, inspired by awesome-NeRF.
TODO
Object
high quality
- DreamFields: Zero-Shot Text-Guided Object Generation with Dream Fields, Jain et al., CVPR 2022 | github | bibtex
- DreamFusion: Text-to-3D using 2D Diffusion, Poole et al., ICLR 2023 | github | bibtex
- Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation, Wang et al., CVPR 2023 |github| bibtex
- RealFusion: 360° Reconstruction of Any Object from a Single Image, Melas-Kyriazi et al., CVPR 2023 | github | bibtex
- 3DFuse: Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation, Seo et al., Arxiv 2023 | github | bibtex
- Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models, Xu et al., CVPR 2023 | bibtex
- Magic3D: High-Resolution Text-to-3D Content Creation, Lin et al., CVPR 2023 | bibtex
- Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation, Chen et al., ICCV 2023 | github | bibtex
- Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior, Tang et al., ICCV 2023 | github | bibtex
- HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance, Zhu et al., Arxiv 2023 | github | bibtex
- MATLABER: Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR, Xu et al., Arxiv 2023 | github | bibtex
- ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation, Wang et al., NeurIPS 2023 | github | bibtex
- DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior, Sun et al., Arxiv 2023 | github | bibtex
- LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching, Liang et al., Arxiv 2023 | github | bibtex
multi-view consistent
- Zero-1-to-3: Zero-shot One Image to 3D Object, Liu et al., ICCV 2023 | github | bibtex
- ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image, Purushwalkam et al., NeurIPS 2023 | bibtex
- One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization, Liu et al., NeurIPS 2023 | github | bibtex
- Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors, Qian et al., Arxiv 2023 | github | bibtex
- SyncDreamer: Generating Multiview-consistent Images from a Single-view Image, Liu et al., Arxiv 2023 | github | bibtex
- MVDream: Multi-view Diffusion for 3D Generation, Shi et al., Arxiv 2023 | github | bibtex
- Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models, Ye et al., 3DV 2024 | bibtex
- Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model, Shi et al., Arxiv 2023 | github | bibtex
- HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation, Huang et al., Arxiv 2023 | bibtex
- Wonder3D: Single Image to 3D using Cross-Domain Diffusion, Long et al., Arxiv 2023 | github | bibtex
- SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D, Li et al., Arxiv 2023 | github | bibtex
- One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion, Liu et al., Arxiv 2023 | github | bibtex
faster
- DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation, Tang et al., Arxiv 2023 | github | bibtex
- Gsgen: Text-to-3D using Gaussian Splatting, Chen et al., Arxiv 2023 | github | bibtex
- LRM: Large Reconstruction Model for Single Image to 3D, Hong et al., Arxiv 2023 | bibtex
- Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model, Li et al., Arxiv 2023 | bibtex
- DMV3D:Denoising Multi-View Diffusion using 3D Large Reconstruction Model
- Instant3D : Instant Text-to-3D Generation, Li et al., Arxiv 2023 | bibtex
- HyperFields:Towards Zero-Shot Generation of NeRFs from Text, Babu et al., Arxiv 2023 | github | bibtex
editing
- DreamBooth3D: Subject-Driven Text-to-3D Generation, Raj et al., ICCV 2023 | bibtex
- TECA: Text-Guided Generation and Editing of Compositional 3D Avatars, Zhang et al., Arxiv 2023 | github | bibtex
- Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D Diffusion-based Editor, Shao et al., Arxiv 2023 | bibtex
- GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting, Chen et al., Arxiv 2023 | github | bibtex
conditional control
- Control3D: Towards Controllable Text-to-3D Generation, Chen et al., ACM Multimedia 2023 | bibtex
- IPDreamer: Appearance-Controllable 3D Object Generation with Image Prompts, Zeng et al., Arxiv 2023 | bibtex
Procedural 3D Modeling
- ProcTHOR: Large-Scale Embodied AI Using Procedural Generation, Deitke et al., NeurIPS 2022 | github | bibtex
- 3D-GPT: Procedural 3D Modeling with Large Language Models, Sun et al., Arxiv 2023 | github | bibtex
3D Native Generative Models
- GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images, Gao et al., NeurIPS 2022 | github | bibtex
- LION: Latent Point Diffusion Models for 3D Shape Generation, Zeng et al., NeurIPS 2022 | github | bibtex
- Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions, Chou et al., ICCV 2023 | github | bibtex
- SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation, Cheng et al., CVPR 2023 | github | bibtex
- DiffRF: Rendering-guided 3D Radiance Field Diffusion, Müller et al., CVPR 2023 | bibtex
- Point-E: A System for Generating 3D Point Clouds from Complex Prompts, Nichol et al., Arxiv 2022 | github | bibtex
- 3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models, Zhang et al., TOG 2023 | github | bibtex
- MeshDiffusion: Score-based Generative 3D Mesh Modeling, Liu et al., ICLR 2023 | github | bibtex
- 3DGen: Triplane Latent Diffusion for Textured Mesh Generation, Gupta et al., Arxiv 2023 | bibtex
- 3D VADER - AutoDecoding Latent 3D Diffusion Models, Ntavelis et al., Arxiv 2023 | github | bibtex
- HoloDiffusion: Training a 3D Diffusion Model using 2D Images, Karnewar et al., CVPR 2023 | github | bibtex
- HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion, Erkoç et al., ICCV 2023 | github | bibtex
- Shap-E: Generating Conditional 3D Implicit Functions, Jun et al., Arxiv 2023 | github | bibtex
- LAS-Diffusion: Locally Attentional SDF Diffusion for Controllable 3D Shape Generation, Zheng et al., TOG 2023 | github | bibtex
- Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation, Zhao et al., Arxiv 2023 | github | bibtex
- ARGUS: Visualization of AI-Assisted Task Guidance in AR, Castelo et al., Arxiv 2023 | bibtex
Scene
- Text2Light: Zero-Shot Text-Driven HDR Panorama Generation, Chen et al., TOG 2022 | github | bibtext
- SceneScape: Text-Driven Consistent Scene Generation, Fridman et al., Arxiv 2023 | github | bibtext
- Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models, Höllein et al., ICCV 2023 | github | bibtext
- Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields, Zhang et al., Arxiv 2023 | github | bibtext
- MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion, Tang et al., NeurIPS 2023 | github | bibtext
- Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints, Fang et al., Arxiv 2023 | github | bibtext
- ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Real Image, Sargent et al., Arxiv 2023 | github | bibtext
- LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes, Chuang et al., Arxiv 2023 | bibtext
- Pyramid Diffusion for Fine 3D Large Scene Generation, Liu et al., Arxiv 2023 | github | bibtext
Dynamic
- TADA! Text to Animatable Digital Avatars, Liao et al., 3DV 2024 | github | bibtext
- Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video, Jiang et al., Arxiv 2023 | github | bibtext
- Text-To-4D Dynamic Scene Generation, Singer et al., Arxiv 2023 | bibtext
- MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion, Kapon et al., Arxiv 2023 | github | bibtext
Texture
- StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions, Höllein et al., CVPR 2022 | github | bibtex
- Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures, Metzer et al., CVPR 2023 | github | bibtex
- TEXTure: Text-Guided Texturing of 3D Shapes, Richardson et al., SIGGRAPH 2023 | github | bibtex
- Text2Tex: Text-driven Texture Synthesis via Diffusion Models, Chen et al., ICCV 2023 | github | bibtex
- TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models, Cao et al., ICCV 2023 | bibtex
- RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture, Song et al., Arxiv 2023 | bibtex
- 3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models, Yang et al., ACM Multimedia 2023 | github | bibtex
- DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagation, Yang et al., Arxiv 2023 | github | bibtext
- Objaverse-XL, Deitke et al., Arxiv 2023 | github | bibtext
- AI 3D Generation, explained, Jia-Bin Huang
- 3D Generation, bilibili, Leo
- Threestudio, Yuan-Chen Guo, 2023 | bibtex
- stable-dreamfusion, Jiaxiang Tang, 2023 | bibtex
- Dream Textures, Carson Katri, 2023
Awesome AIGC 3D is released under the MIT license.
contact: hitcslj@stu.hit.edu.cn.