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We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation.
Texture and reflection in computer generated images
James F Blinn and Martin E Newell · 1976
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Zippered polygon meshes from range images
Greg Turk and Marc Levoy · 1994
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Generating typed dependency parses from phrase structure parses
Marie-Catherine De Marneffe, Bill MacCartney, Christopher D Manning, et al · 2006
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Mining of massive datasets
Anand Rajaraman and Jeffrey David Ullman · 2011
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Minimal recursion: exploring the prospects
Noam Chomsky · 2014
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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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An improved non-monotonic transition system for dependency parsing
Matthew Honnibal and Mark Johnson · 2015
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Deep convolutional neural fields for depth estimation from a single image
Fayao Liu, Chunhua Shen, and Guosheng Lin · 2015
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese · 2016
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Unsupervised learning of 3d structure from images
Danilo Jimenez Rezende, SM Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J Guibas · 2017
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Deep learning for image-to-text generation: A technical overview
Xiaodong He and Li Deng · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Text2shape: Generating shapes from natural language by learning joint embeddings
Kevin Chen, Christopher B Choy, Manolis Savva, Angel X Chang, Thomas Funkhouser, and Silvio Savarese · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Keep drawing it: Iterative language-based image generation and editing
Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, and Graham W Taylor · 2018
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Constituency parsing with a self-attentive encoder
Nikita Kitaev and Dan Klein · 2018
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Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabas Poczos, and Ruslan Salakhutdinov · 2018
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Pixel2mesh: Generating 3d mesh models from single rgb images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang · 2018
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Attngan: Fine-grained text to image generation with attentional generative adversarial networks
Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He · 2018
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From hand to mouth
Michael C Corballis · 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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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis
Ming Tao, Hao Tang, Songsong Wu, Nicu Sebe, Xiao-Yuan Jing, Fei Wu, and Bingkun Bao · 2020
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Voxel2mesh: 3d mesh model generation from volumetric data
Udaranga Wickramasinghe, Edoardo Remelli, Graham Knott, and Pascal Fua · 2020
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Cnns on surfaces using rotation-equivariant features
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ShapeGlot: Learning language for shape differentiation
Panos Achlioptas, Judy Fan, X.D. Robert Hawkins, D. Noah Goodman, and J. Leonidas Guibas · 2019
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Scan2mesh: From unstructured range scans to 3d meshes
Angela Dai and Matthias Nießner · 2019
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Mesh r-cnn
Georgia Gkioxari, Jitendra Malik, and Justin Johnson · 2019
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Meshcnn: a network with an edge
Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, and Daniel Cohen-Or · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Controllable text-to-image generation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, and Philip H. S. Torr · 2019
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Ruben Wiersma, Elmar Eisemann, and Klaus Hildebrandt · 2020
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Adversarial text-to-image synthesis: A review
Stanislav Frolov, Tobias Hinz, Federico Raue, Jörn Hees, and Andreas Dengel · 2021
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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 · 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, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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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, and Marco Fumero · 2021
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Neural geometric level of detail: Real-time rendering with implicit 3d shapes
Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler · 2021
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Pixeltransformer: Sample conditioned signal generation
Shubham Tulsiani and Abhinav Gupta · 2021
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Clip-nerf: Text-and-image driven manipulation of neural radiance fields
Can Wang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao · 2021
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Improving text-to-image synthesis using contrastive learning
Hui Ye, Xiulong Yang, Martin Takac, Rajshekhar Sunderraman, and Shihao Ji · 2021
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Cross-modal contrastive learning for text-to-image generation
Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee, and Yinfei Yang · 2021
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Towards implicit text-guided 3d shape generation
Zhengzhe Liu, Yi Wang, Xiaojuan Qi, and Chi-Wing Fu · 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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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Shapeformer: Transformer-based shape completion via sparse representation
Xingguang Yan, Liqiang Lin, Niloy J Mitra, Dani Lischinski, Danny Cohen-Or, and Hui Huang · 2022
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