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Using the latent diffusion model has proven effective in developing novel 3D generation techniques.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent · 2000
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Surface parameterization: a tutorial and survey
Michael S. Floater and Kai Hormann · 2005
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Shapenet: An information-rich 3d model repository
Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, L. Yi, and Fisher Yu · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J. Guibas · 2017
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J. Guibas · 2018
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Blender - a 3D modelling and rendering package
Blender Online Community · 2018
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A papier-mâché approach to learning 3d surface generation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Learning elementary structures for 3d shape generation and matching
Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir Kim, Bryan Russell, and Mathieu Aubry · 2019
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Morphing and sampling network for dense point cloud completion
Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and Shi-Min Hu · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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Structurenet: Hierarchical graph networks for 3d shape generation
Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, and Leonidas Guibas · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
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Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
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Bsp-net: Generating compact meshes via binary space partitioning
Zhiqin Chen, Andrea Tagliasacchi, and Hao Zhang · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Polygen: An autoregressive generative model of 3d meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, and Peter W. Battaglia · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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CLIPScore: a reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
Generative pointnet: Deep energy-based learning on unordered point sets for 3d generation, reconstruction and classification
Jianwen Xie, Yifei Xu, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Zhu Song-Chun, and Ying Nian Wu · 2021
Cited alongside, same era.
3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
Cited alongside, same era.
Efficient geometry-aware 3d generative adversarial networks
Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J. Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein · 2022
Scalable 3d captioning with pretrained models
Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson · 2023
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Diffrf: Rendering-guided 3d radiance field diffusion
Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, and Matthias Nießner · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
Later among the works it cites.
Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to-3d
Lingteng Qiu, Guanying Chen, Xiaodong Gu, Qi zuo, Mutian Xu, Yushuang Wu, Weihao Yuan, Zilong Dong, Liefeng Bo, and Xiaoguang Han · 2023
Later among the works it cites.
3d neural field generation using triplane diffusion
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Cited alongside, same era.
Objaverse: A universe of annotated 3d objects
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi · 2022
Cited alongside, same era.
Neural points: Point cloud representation with neural fields for arbitrary upsampling
Wanquan Feng, Jin Li, Hongrui Cai, Xiaonan Luo, and Juyong Zhang · 2022
Cited alongside, same era.
GET3d: A generative model of high quality 3d textured shapes learned from images
Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler · 2022
Cited alongside, same era.
Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Neural wavelet-domain diffusion for 3d shape generation
Ka-Hei Hui, Ruihui Li, Jingyu Hu, and Chi-Wing Fu · 2022
Cited alongside, same era.
Zero-shot text-guided object generation with dream fields
Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole · 2022
Cited alongside, same era.
J. Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, and Gordon Wetzstein · 2023
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Meshgpt: Generating triangle meshes with decoder-only transformers
Yawar Siddiqui, Antonio Alliegro, Alexey Artemov, Tatiana Tommasi, Daniele Sirigatti, Vladislav Rosov, Angela Dai, and Matthias Nießner · 2023
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Dreamcraft3d: Hierarchical 3d generation with bootstrapped diffusion prior
Jingxiang Sun, Bo Zhang, Ruizhi Shao, Lizhen Wang, Wen Liu, Zhenda Xie, and Yebin Liu · 2023
Later among the works it cites.
Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation
Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich · 2023
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Imagedream: Image-prompt multi-view diffusion for 3d generation
Peng Wang and Yichun Shi · 2023
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Novel view synthesis with diffusion models
Daniel Watson, William Chan, Ricardo Martin Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi · 2023
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Locally attentional sdf diffusion for controllable 3d shape generation
Xin-Yang Zheng, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu, and Heung-Yeung Shum · 2023
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Large-vocabulary 3d diffusion model with transformer
Ziang Cao, Fangzhou Hong, Tong Wu, Liang Pan, and Ziwei Liu · 2024
Closest in time.
V3d: Video diffusion models are effective 3d generators
Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, and Huaping Liu · 2024
Closest in time.
Vfusion3d: Learning scalable 3d generative models from video diffusion models
Junlin Han, Filippos Kokkinos, and Philip Torr · 2024
Closest in time.
Gvgen: Text-to-3d generation with volumetric representation, 2024
Xianglong He, Junyi Chen, Sida Peng, Di Huang, Yangguang Li, Xiaoshui Huang, Chun Yuan, Wanli Ouyang, and Tong He · 2024
Closest in time.
LRM: Large reconstruction model for single image to 3d
Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan · 2024
Closest in time.
2d gaussian splatting for geometrically accurate radiance fields
Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao · 2024
Closest in time.
LEAP: Liberate sparse-view 3d modeling from camera poses
Hanwen Jiang, Zhenyu Jiang, Yue Zhao, and Qixing Huang · 2024
Closest in time.
Ln3diff: Scalable latent neural fields diffusion for speedy 3d generation, 2024
Yushi Lan, Fangzhou Hong, Shuai Yang, Shangchen Zhou, Xuyi Meng, Bo Dai, Xingang Pan, and Chen Change Loy · 2024
Closest in time.
One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization
Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su · 2024
Closest in time.
DINOv2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2024
Closest in time.
Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies
Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams · 2024
Closest in time.
L3dg: Latent 3d gaussian diffusion
Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Angela Dai, and Matthias Nießner · 2024
Closest in time.
Splatter image: Ultra-fast single-view 3d reconstruction
Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi · 2024
Closest in time.
Lgm: Large multi-view gaussian model for high-resolution 3d content creation
Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu · 2024
Closest in time.
Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu · 2024
Closest in time.
Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation, 2024
Xu Yinghao, Shi Zifan, Yifan Wang, Chen Hansheng, Yang Ceyuan, Peng Sida, Shen Yujun, and Wetzstein Gordon · 2024
Closest in time.
Gaussian opacity fields: Efficient high-quality compact surface reconstruction in unbounded scenes
Zehao Yu, Torsten Sattler, and Andreas Geiger · 2024
Closest in time.
Clay: A controllable large-scale generative model for creating high-quality 3d assets
Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu · 2024
Closest in time.
Diffgs: Functional gaussian splatting diffusion
Junsheng Zhou, Weiqi Zhang, and Yu-Shen Liu · 2024
Closest in time.
Zi-Xin Zou, Zhipeng Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang, Yan-Pei Cao, and Song-Hai Zhang · 2024
Closest in time.
Gaussiananything: Interactive point cloud latent diffusion for 3d generation
Yushi Lan, Shangchen Zhou, Zhaoyang Lyu, Fangzhou Hong, Shuai Yang, Bo Dai, Xingang Pan, and Chen Change Loy · 2025
Closest in time.