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Recent years have seen an explosion of work and interest in text-to-3D shape generation.
“DeepSDF: Learning continuous signed distance functions for shape representation”
Jeong Park et al · 1901
Earlier work this paper cites.
“ShapeGlot: Learning language for shape differentiation”
Panos Achlioptas et al · 1905
Earlier work this paper cites.
“Differentiable volumetric rendering: Learning implicit 3D representations without 3D supervision”
Michael Niemeyer, Lars Mescheder, Michael Oechsle and Andreas Geiger · 1912
Earlier work this paper cites.
“An empirical Bayes approach to statistics”
Herbert Robbins · 1992
Earlier work this paper cites.
“Marching cubes: A high resolution 3D surface construction algorithm”
William Lorensen and Harvey Cline · 1998
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“NeRF: Representing scenes as neural radiance fields for view synthesis”
Ben Mildenhall et al · 2003
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“Denoising diffusion probabilistic models”
Jonathan Ho, Ajay Jain and Pieter Abbeel · 2006
Earlier work this paper cites.
“Poisson surface reconstruction”
Michael Kazhdan, Matthew Bolitho and Hugues Hoppe · 2006
Earlier work this paper cites.
“Learning deformable tetrahedral meshes for 3D reconstruction”
Jun Gao et al · 2011
Earlier work this paper cites.
“Modular primitives for high-performance differentiable rendering”
Samuli Laine et al · 2011
Earlier work this paper cites.
“Score-based generative modeling through stochastic differential equations”
Yang Song et al · 2011
Earlier work this paper cites.
“Auto-encoding variational bayes”
Diederik Kingma and Max Welling · 2013
Earlier work this paper cites.
“Conditional generative adversarial nets”
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
“ShapeNet: An information-rich 3D model repository”
Angel Chang et al · 2015
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
Earlier work this paper cites.
“Deep unsupervised learning using nonequilibrium thermodynamics”
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan and Surya Ganguli · 2015
Earlier work this paper cites.
“Density estimation using real NVP”
Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio · 2016
Earlier work this paper cites.
“Improved techniques for training GANs”
Tim Salimans et al · 2016
Earlier work this paper cites.
“Improved deep metric learning with multi-class N-pair loss objective”
Kihyuk Sohn · 2016
Earlier work this paper cites.
“Wasserstein generative adversarial networks”
Martin Arjovsky, Soumith Chintala and Léon Bottou · 2017
Earlier work this paper cites.
“Improved training of Wasserstein GANs”
Ishaan Gulrajani et al · 2017
Earlier work this paper cites.
David Ha, Andrew Dai and Quoc Le · 2017
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“Learning by association–A versatile semi-supervised training method for neural networks”
Philip Haeusser, Alexander Mordvintsev and Daniel Cremers · 2017
Earlier work this paper cites.
“GANs trained by a two time-scale update rule converge to a local nash equilibrium”
Martin Heusel et al · 2017
Earlier work this paper cites.
“Neural discrete representation learning”
Aaron Van and Oriol Vinyals · 2017
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“Learning representations and generative models for 3D point clouds”
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas and Leonidas Guibas · 2018
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“Implicit maximum likelihood estimation”
Ke Li and Jitendra Malik · 2018
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“Text2Shape: Generating shapes from natural language by learning joint embeddings”
Kevin Chen et al · 2019
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“Occupancy networks: Learning 3D reconstruction in function space”
Lars Mescheder et al · 2019
Earlier work this paper cites.
“Learning generative models of 3D structures”
Siddhartha Chaudhuri et al · 2020
Earlier work this paper cites.
“Mip-NeRF: A multiscale representation for anti-aliasing neural radiance fields”
Jonathan Barron et al · 2021
Earlier work this paper cites.
“Diffusion models beat gans on image synthesis”
Prafulla Dhariwal and Alexander Nichol · 2021
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“VLGrammar: Grounded grammar induction of vision and language”
Yining Hong, Qing Li, Song-Chun Zhu and Siyuan Huang · 2021
Earlier work this paper cites.
“LoRA: Low-rank adaptation of large language models”
Edward Hu et al · 2021
Earlier work this paper cites.
“SDEdit: Guided image synthesis and editing with stochastic differential equations”
Chenlin Meng et al · 2021
Earlier work this paper cites.
“GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models”
Alex Nichol et al · 2021
Earlier work this paper cites.
“Benchmark for compositional text-to-image synthesis”
Dong Park et al · 2021
Earlier work this paper cites.
“Vision transformers for dense prediction”
René Ranftl, Alexey Bochkovskiy and Vladlen Koltun · 2021
Earlier work this paper cites.
“Learning transferable visual models from natural language supervision”
Alec Radford et al · 2021
Earlier work this paper cites.
“Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis”
Tianchang Shen et al · 2021
Earlier work this paper cites.
“NeuS: Learning neural implicit surfaces by volume rendering for multi-view reconstruction”
Peng Wang et al · 2021
Earlier work this paper cites.
“Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields”
Jonathan. Barron et al · 2022
Earlier work this paper cites.
“eDiffI: Text-to-image diffusion models with an ensemble of expert denoisers”
Yogesh Balaji et al · 2022
Earlier work this paper cites.
“LDEdit: Towards generalized text guided image manipulation via latent diffusion models”
Paramanand Chandramouli and Kanchana Gandikota · 2022
Earlier work this paper cites.
“ABO: Dataset and benchmarks for real-world 3D object understanding”
Jasmine Collins et al · 2022
Earlier work this paper cites.
“TensoRF: Tensorial radiance fields”
Anpei Chen et al · 2022
Earlier work this paper cites.
“Plenoxels: Radiance fields without neural networks”
Sara Fridovich-Keil et al · 2022
Earlier work this paper cites.
“ShapeCrafter: A recursive text-conditioned 3D shape generation model”
Rao Fu et al · 2022
Earlier work this paper cites.
“GET3D: A generative model of high quality 3D textured shapes learned from images”
Jun Gao et al · 2022
Earlier work this paper cites.
“Classifier-free diffusion guidance”
Jonathan Ho and Tim Salimans · 2022
Earlier work this paper cites.
“Zero-shot text-guided object generation with dream fields”
Ajay Jain et al · 2022
Earlier work this paper cites.
“Elucidating the design space of diffusion-based generative models”
Tero Karras, Miika Aittala, Timo Aila and Samuli Laine · 2022
Earlier work this paper cites.
“DiffusionCLIP: Text-guided diffusion models for robust image manipulation”
Gwanghyun Kim, Taesung Kwon and Jong Ye · 2022
Cited alongside, same era.
“ReLU fields: The little non-linearity that could”
Animesh Karnewar, Tobias Ritschel, Oliver Wang and Niloy Mitra · 2022
Cited alongside, same era.
“Understanding pure clip guidance for voxel grid nerf models”
Han-Hung Lee and Angel Chang · 2022
Cited alongside, same era.
“Repaint: Inpainting using denoising diffusion probabilistic models”
Andreas Lugmayr et al · 2022
Cited alongside, same era.
“Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program”
“UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation”
Zexiang Liu et al · 2023
Later among the works it cites.
“SyncDreamer: Generating Multiview-consistent Images from a Single-view Image”
Yuan Liu et al · 2023
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“Scalable 3D Captioning with Pretrained Models”
Tiange Luo, Chris Rockwell, Honglak Lee and Justin Johnson · 2023
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Minghua Liu et al · 2023
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“OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding”
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Tiange Luo, Honglak Lee and Justin Johnson · 2022
Cited alongside, same era.
“SparseNeuS: Fast generalizable neural surface reconstruction from sparse views”
Xiaoxiao Long et al · 2022
Cited alongside, same era.
“Towards implicit text-guided 3D shape generation”
Zhengzhe Liu, Yi Wang, Xiaojuan Qi and Chi-Wing Fu · 2022
Cited alongside, same era.
“Text2mesh: Text-driven neural stylization for meshes”
Oscar Michel et al · 2022
Cited alongside, same era.
“AutoSDF: Shape priors for 3D completion, reconstruction and generation”
Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh and Shubham Tulsiani · 2022
Cited alongside, same era.
“Instant Neural Graphics Primitives with a Multiresolution Hash Encoding”
Thomas Müller, Alex Evans, Christoph Schied and Alexander Keller · 2022
Cited alongside, same era.
“CLIP-mesh: Generating textured meshes from text using pretrained image-text models”
Nasir Mohammad, Tianhao Xie, Eugene Belilovsky and Tiberiu Popa · 2022
Cited alongside, same era.
“Point-E: A system for generating 3D point clouds from complex prompts”
Alex Nichol et al · 2022
Cited alongside, same era.
Minghua Liu et al · 2023
Later among the works it cites.
“Instant3D: Fast text-to-3D with sparse-view generation and large reconstruction model”
Jiahao Li et al · 2023
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“Zero-1-to-3: Zero-shot one image to 3D object”
Ruoshi Liu et al · 2023
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“One-2-3-45: Any single image to 3D mesh in 45 seconds without per-shape optimization”
Minghua Liu et al · 2023
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“ATT3D: Amortized Text-to-3D Object Synthesis”
Jonathan Lorraine et al · 2023
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“LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching”
Yixun Liang et al · 2023
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“Instant3D: Instant Text-to-3D Generation”
Ming Li et al · 2023
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“Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5 D Diffusion”
Yuanxun Lu et al · 2023
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“Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era”
Chenghao Li et al · 2023
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“SKED: Sketch-guided Text-based 3D Editing”
Aryan Mikaeili, Or Perel, Daniel Cohen-Or and Ali Mahdavi-Amiri · 2023
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“Latent-NeRF for shape-guided generation of 3D shapes and textures”
Gal Metzer et al · 2023
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“DreamFusion: Text-to-3D using 2D Diffusion”
Ben Poole, Ajay Jain, Jonathan. Barron and Ben Mildenhall · 2023
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“Compositional 3D scene generation using locally conditioned diffusion”
Ryan Po and Gordon Wetzstein · 2023
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“Neurosymbolic Models for Computer Graphics”
Daniel Ritchie et al · 2023
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“TEXTure: Text-guided texturing of 3D shapes”
Elad Richardson et al · 2023
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“RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture”
Liangchen Song et al · 2023
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“Zero123++: a single image to consistent multi-view diffusion base model”
Ruoxi Shi et al · 2023
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“Vox-E: Text-guided Voxel Editing of 3D Objects”
Etai Sella, Gal Fiebelman, Peter Hedman and Hadar Averbuch-Elor · 2023
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“CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes From Natural Language”
Aditya Sanghi et al · 2023
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“Let 2D diffusion model know 3D-consistency for robust text-to-3D generation”
Junyoung Seo et al · 2023
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“MVDream: Multi-view diffusion for 3D generation”
Yichun Shi et al · 2023
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“TextMesh: Generation of Realistic 3D Meshes From Text Prompts”
Christina Tsalicoglou et al · 2023
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“DreamGaussian: Generative gaussian splatting for efficient 3D content creation”
Jiaxiang Tang et al · 2023
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“Stable Score Distillation for High-Quality 3D Generation”
Boshi Tang, Jianan Wang, Zhiyong Wu and Lei Zhang · 2023
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“ShapeScaffolder: Structure-Aware 3D Shape Generation from Text”
Xi Tian, Yong-Liang Yang and Qi Wu · 2023
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“CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting”
Alexander Vilesov, Pradyumna Chari and Achuta Kadambi · 2023
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“Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation”
Haochen Wang et al · 2023
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“SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity”
Peihao Wang et al · 2023
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Zhengyi Wang et al · 2023
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“TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo Supervision”
Jiacheng Wei et al · 2023
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“Taming Mode Collapse in Score Distillation for Text-to-3D Generation”
Peihao Wang et al · 2023
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“ULIP: Learning a unified representation of language, images, and point clouds for 3D understanding”
Le Xue et al · 2023
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“DMV3D: Denoising multi-view diffusion using 3D large reconstruction model”
Yinghao Xu et al · 2023
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“Dream3D: Zero-shot text-to-3D synthesis using 3D shape prior and text-to-image diffusion models”
Jiale Xu et al · 2023
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“GaussianDreamer: Fast Generation from Text to 3D Gaussian Splatting with Point Cloud Priors”
Taoran Yi et al · 2023
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“Text-to-3D with classifier score distillation”
Xin Yu et al · 2023
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“CADTalk: An Algorithm and Benchmark for Semantic Commenting of CAD Programs”
Haocheng Yuan et al · 2023
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Zibo Zhao et al · 2023
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“Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields”
Jingbo Zhang et al · 2023
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“Locally attentional SDF diffusion for controllable 3D shape generation”
Xin-Yang Zheng et al · 2023
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“DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling”
Linqi Zhou, Andy Shih, Chenlin Meng and Stefano Ermon · 2023
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“SceneWiz3D: Towards Text-guided 3D Scene Composition”
Qihang Zhang et al · 2023
Later among the works it cites.
“GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs”
Gege Gao et al · 2024
Closest in time.
“Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping”
Zijie Pan, Jiachen Lu, Xiatian Zhu and Li Zhang · 2024
Closest in time.
“GPT-4V (ision) is a Human-Aligned Evaluator for Text-to-3D Generation”
Tong Wu et al · 2024
Closest in time.
Zike Wu et al · 2024
Closest in time.
“Learning spatial knowledge for text to 3D scene generation”
Angel Chang, Manolis Savva and Christopher Manning · 2038
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