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Generative models for 3D object synthesis have seen significant advancements with the incorporation of prior knowledge distilled from 2D diffusion models.
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, 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.
Learning a predictable and generative vector representation for objects
Rohit Girdhar, David F Fouhey, Mikel Rodriguez, and Abhinav Gupta · 2016
Earlier work this paper cites.
A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
3d menagerie: Modeling the 3d shape and pose of animals
Silvia Zuffi, Angjoo Kanazawa, David W Jacobs, and Michael J Black · 2017
Earlier work this paper cites.
Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
Earlier work this paper cites.
Pixel2mesh: Generating 3d mesh models from single rgb images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang · 2018
Earlier work this paper cites.
Lions and tigers and bears: Capturing non-rigid, 3d, articulated shape from images
Silvia Zuffi, Angjoo Kanazawa, and Michael J Black · 2018
Earlier work this paper cites.
Expressive body capture: 3d hands, face, and body from a single image
Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black · 2019
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Pixel2mesh++: Multi-view 3d mesh generation via deformation
Chao Wen, Yinda Zhang, Zhuwen Li, and Yanwei Fu · 2019
Earlier work this paper cites.
Pix2vox: Context-aware 3d reconstruction from single and multi-view images
Haozhe Xie, Hongxun Yao, Xiaoshuai Sun, Shangchen Zhou, and Shengping Zhang · 2019
Earlier work this paper cites.
Disn: Deep implicit surface network for high-quality single-view 3d reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Earlier work this paper cites.
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, et al · 2021
Cited alongside, same era.
Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny · 2021
Cited alongside, same era.
Nerf–: Neural radiance fields without known camera parameters
Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu · 2021
Cited alongside, same era.
An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
Cited alongside, same era.
Neural 3d scene reconstruction with the manhattan-world assumption
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 · 2023
Closest in time.
threestudio: A unified framework for 3d content generation
Yuan-Chen Guo, Ying-Tian Liu, Ruizhi Shao, Christian Laforte, Vikram Voleti, Guan Luo, Chia-Hao Chen, Zi-Xin Zou, Chen Wang, Yan-Pei Cao, and Song-Hai Zhang · 2023
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Sparf: Large-scale learning of 3d sparse radiance fields from few input images
Abdullah Hamdi, Bernard Ghanem, and Matthias Nießsner · 2023
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T3 bench: Benchmarking current progress in text-to-3d generation
Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, and Yong-Jin Liu · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, and Xiaowei Zhou · 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.
Clip-mesh: Generating textured meshes from text using pretrained image-text models
Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa · 2022
Cited alongside, same era.
Autorf: Learning 3d object radiance fields from single view observations
Norman Müller, Andrea Simonelli, Lorenzo Porzi, Samuel Rota Bulo, Matthias Nießner, and Peter Kontschieder · 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.
Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Closest in time.
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 · 2023
Closest in time.
Zero-1-to-3: Zero-shot one image to 3d object
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick · 2023
Closest in time.
Realfusion: 360deg reconstruction of any object from a single image
Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, and Andrea Vedaldi · 2023
Closest in time.
Latent-nerf for shape-guided generation of 3d shapes and textures
Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or · 2023
Closest in time.
Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior
Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen · 2023
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Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation
Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich · 2023
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 · 2023
Closest in time.
Multiview compressive coding for 3d reconstruction
Chao-Yuan Wu, Justin Johnson, Jitendra Malik, Christoph Feichtenhofer, and Georgia Gkioxari · 2023
Closest in time.
Neurallift-360: Lifting an in-the-wild 2d photo to a 3d object with 360deg views
Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Yi Wang, and Zhangyang Wang · 2023
Closest in time.