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Tremendous progress in deep generative models has led to photorealistic image synthesis.
Ray tracing volume densities
James T. Kajiya and Brian Von Herzen · 1984
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Shape and motion under varying illumination: Unifying structure from motion, photometric stereo, and multi-view stereo
Li Zhang, Brian Curless, Aaron Hertzmann, and Steven M. Seitz · 2003
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Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Learning to disentangle factors of variation with manifold interaction
Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 2014
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Semantic image segmentation via deep parsing network
Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2015
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Learning a discriminative model for the perception of realism in composite images
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A. Efros · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Generating images part by part with composite generative adversarial networks
Hanock Kwak and Byoung-Tak Zhang · 2016
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Unsupervised learning of 3d structure from images
Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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Generative image modeling using style and structure adversarial networks
Xiaolong Wang and Abhinav Gupta · 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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3d shape induction from 2d views of multiple objects
Matheus Gadelha, Subhransu Maji, and Rui Wang · 2017
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Geometric image synthesis
Hassan Alhaija, Siva Mustikovela, Andreas Geiger, and Carsten Rother · 2018
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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An intriguing failing of convolutional neural networks and the coordconv solution
Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski · 2018
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Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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Photoshape: Photorealistic materials for large-scale shape collections
Keunhong Park, Konstantinos Rematas, Ali Farhadi, and Steven M. Seitz · 2018
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Modular generative adversarial networks
Bo Zhao, Bo Chang, Zequn Jie, and Leonid Sigal · 2018
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Visual object networks: Image generation with disentangled 3d representations
Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, and Bill Freeman · 2018
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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BAE-NET: branched autoencoder for shape co-segmentation
Zhiqin Chen, Kangxue Yin, Matthew Fisher, Siddhartha Chaudhuri, and Hao Zhang · 2019
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Learning shape templates with structured implicit functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T Freeman, and Thomas Funkhouser · 2019
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Ganalyze: Toward visual definitions of cognitive image properties
Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola · 2019
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Recurrent independent mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, and Bernhard Schölkopf · 2019
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Learning single-image 3d reconstruction by generative modelling of shape, pose and shading
Paul Henderson and Vittorio Ferrari · 2019
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Escaping plato’s cave: 3d shape from adversarial rendering
Philipp Henzler, Niloy J Mitra, , and Tobias Ritschel · 2019
Ganspace: Discovering interpretable GAN controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
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Unsupervised object-centric video generation and decomposition in 3d
Paul Henderson and Christoph H. Lampert · 2020
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Leveraging 2d data to learn textured 3d mesh generation
Paul Henderson, Vagia Tsiminaki, and Christoph H. Lampert · 2020
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On the ”steerability” of generative adversarial networks
Ali Jahanian, Lucy Chai, and Phillip Isola · 2020
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Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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High-fidelity synthesis with disentangled representation
Wonkwang Lee, Donggyun Kim, Seunghoon Hong, and Honglak Lee · 2020
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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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Learning to infer implicit surfaces without 3d supervision
Shichen Liu, Shunsuke Saito, Weikai Chen, and Hao Li · 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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Implicit surface representations as layers in neural networks
Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson · 2019
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Hologan: Unsupervised learning of 3d representations from natural images
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang · 2019
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Hologan: Unsupervised learning of 3d representations from natural images
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang · 2019
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An analysis of SVD for deep rotation estimation
Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, and Ameesh Makadia · 2020
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Towards unsupervised learning of generative models for 3d controllable image synthesis
Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas Geiger · 2020
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Generative adversarial networks for image and video synthesis: Algorithms and applications
Ming-Yu Liu, Xun Huang, Jiahui Yu, Ting-Chun Wang, and Arun Mallya · 2020
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DIST: rendering deep implicit signed distance function with differentiable sphere tracing
Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui · 2020
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Inverse graphics GAN: learning to generate 3d shapes from unstructured 2d data
Sebastian Lunz, Yingzhen Li, Andrew W. Fitzgibbon, and Nate Kushman · 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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Blockgan: Learning 3d object-aware scene representations from unlabelled images
Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, and Niloy Mitra · 2020
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Giraffe: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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The hessian penalty: A weak prior for unsupervised disentanglement
William S. Peebles, John Peebles, Jun-Yan Zhu, Alexei A. Efros, and Antonio Torralba · 2020
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Graf: Generative radiance fields for 3d-aware image synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
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Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2020
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Multiview neural surface reconstruction by disentangling geometry and appearance
Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Ronen Basri, and Yaron Lipman · 2020
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iNeRF: Inverting neural radiance fields for pose estimation
Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin · 2020
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Nerf++: Analyzing and improving neural radiance fields
Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun · 2020
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Image gans meet differentiable rendering for inverse graphics and interpretable 3d neural rendering
Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, and Sanja Fidler · 2020
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A-NeRF: Surface-free human 3d pose refinement via neural rendering
Shih-Yang Su, Frank Yu, Michael Zollhoefer, and Helge Rhodin · 2021
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Nerf–: Neural radiance fields without known camera parameters
Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu · 2021
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