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Latent diffusion models for image generation have crossed a quality threshold which enabled them to achieve mass adoption.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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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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Texture fields: Learning texture representations in function space
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger · 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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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Modular primitives for high-performance differentiable rendering
Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Openclip, July 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis, 2021
Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, and Sanja Fidler · 2021
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3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
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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 Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein · 2022
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Diffusionsdf: Conditional generative modeling of signed distance functions, 2022
Gene Chou, Yuval Bahat, and Felix Heide · 2022
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Gensdf: Two-stage learning of generalizable signed distance functions
Gene Chou, Ilya Chugunov, and Felix Heide · 2022
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Objaverse: A universe of annotated 3d objects, 2022
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi · 2022
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Shapecrafter: A recursive text-conditioned 3d shape generation model
Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, and Srinath Sridhar · 2022
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Get3d: A generative model of high quality 3d textured shapes learned from images, 2022
Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler · 2022
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Point-e: A system for generating 3d point clouds from complex prompts
Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding, 2022
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi · 2022
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Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2022
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Classifier-free diffusion guidance, 2022
Jonathan Ho and Tim Salimans · 2022
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Zero-shot text-guided object generation with dream fields
Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, and Ben Poole · 2022
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Clip-mesh: Generating textured meshes from text using pretrained image-text models
Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa · 2022
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Understanding pure clip guidance for voxel grid nerf models
Han-Hung Lee and Angel X Chang · 2022
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Magic3d: High-resolution text-to-3d content creation
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin · 2022
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Autosdf: Shape priors for 3d completion, reconstruction and generation
Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, and Shubham Tulsiani · 2022
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Clip-forge: Towards zero-shot text-to-shape generation
Aditya Sanghi, Hang Chu, Joseph G Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, and Kamal Rahimi Malekshan · 2022
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3d neural field generation using triplane diffusion
J Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, and Gordon Wetzstein · 2022
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Rodin: A generative model for sculpting 3d digital avatars using diffusion, 2022
Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Baltrusaitis, Jingjing Shen, Dong Chen, Fang Wen, Qifeng Chen, and Baining Guo · 2022
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Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models
Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao · 2022
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3dilg: Irregular latent grids for 3d generative modeling
Biao Zhang, Matthias Nießner, and Peter Wonka · 2022
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Fast sampling of diffusion models with exponential integrator, 2022
Qinsheng Zhang and Yongxin Chen · 2022
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Sdf-stylegan: Implicit sdf-based stylegan for 3d shape generation
Xin-Yang Zheng, Yang Liu, Peng-Shuai Wang, and Xin Tong · 2022
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