L3DG: Latent 3D Gaussian Diffusion

HoloGAN: Unsupervised learning of 3D representations from natural images

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Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis

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Ray tracing volume densities

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Image quality assessment: from error visibility to structural similarity

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Denoising Diffusion Probabilistic Models

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GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

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Learning Gradient Fields for Shape Generation

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Generative Adversarial Networks

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Adam: A Method for Stochastic Optimization

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Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , Yoshua Bengio and Yann LeCun (Eds.)

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Tanks and temples: Benchmarking large-scale scene reconstruction

Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. 2017 · 2017

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Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs. In IEEE International Conference on Computer Vision (ICCV)

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Neural discrete representation learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17) . Curran Associates Inc., Red Hook, NY, USA, 6309–6318

Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. 2017 · 2017

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Learning Representations and Generative Models for 3D Point Clouds

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Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. 2018 · 2018

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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

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Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2018 · 2018

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PhotoShape: Photorealistic Materials for Large-Scale Shape Collections

Keunhong Park, Konstantinos Rematas, Ali Farhadi, and Steven M. Seitz. 2018 · 2018

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4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 3075–3084

Christopher Choy, JunYoung Gwak, and Silvio Savarese. 2019 · 2019

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pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. In arXiv

Eric Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. 2020 · 2020

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Generative Sparse Detection Networks for 3D Single-shot Object Detection. In European conference on computer vision

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Nerf: Representing scenes as neural radiance fields for view synthesis. In European conference on computer vision . Springer, 405–421

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2020 · 2020

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High-fidelity performance metrics for generative models in PyTorch

Anton Obukhov, Maximilian Seitzer, Po-Wei Wu, Semen Zhydenko, Jonathan Kyl, and Elvis Yu-Jing Lin. 2020 · 2020

Cited alongside, same era.

Demystifying MMD GANs

Original

Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel, and Arthur Gretton. 2021 · 2021

VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids

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Katja Schwarz, Axel Sauer, Michael Niemeyer, Yiyi Liao, and Andreas Geiger. 2022 · 2022

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EpiGRAF: Rethinking training of 3D GANs

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Ivan Skorokhodov, Sergey Tulyakov, Yiqun Wang, and Peter Wonka. 2022 · 2022

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Novel View Synthesis with Diffusion Models

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Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. 2022 · 2022

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LION: Latent Point Diffusion Models for 3D Shape Generation

Original

Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. 2022 · 2022

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