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Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise as a high-fidelity and compact representation.
Marching cubes: A high resolution 3d surface construction algorithm
William E. Lorensen and Harvey E. Cline · 1987
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Shapenet: An information-rich 3d model repository
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3d shapenets: A deep representation for volumetric shapes
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Decoupled weight decay regularization
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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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Modeling facial geometry using compositional vaes
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Fast winding numbers for soups and clouds
Gavin Barill, Neil G Dickson, Ryan Schmidt, David IW Levin, and Alec Jacobson · 2018
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Codeslam—learning a compact, optimisable representation for dense visual slam
Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J Davison · 2018
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Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 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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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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Deep local shapes: Learning local sdf priors for detailed 3d reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Local implicit grid representations for 3d scenes
Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser, et al · 2020
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Analyzing and improving the image quality of StyleGAN
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
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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 · 2021
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pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein · 2021
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 J Guibas, Jonathan Tremblay, Sameh Khamis, et al · 2022
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Diffusionsdf: Conditional generative modeling of signed distance functions
Gene Chou, Yuval Bahat, and Felix Heide · 2022
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From data to functa: Your data point is a function and you should treat it like one
Emilien Dupont, Hyunjik Kim, SM Eslami, Danilo Rezende, and Dan Rosenbaum · 2022
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Get3d: A generative model of high quality 3d textured shapes learned from images
Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler · 2022
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Diffusion models beat gans on image synthesis
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Learning signal-agnostic manifolds of neural fields
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Generative models as distributions of functions
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Alias-free generative adversarial networks
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4dcomplete: Non-rigid motion estimation beyond the observable surface
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Diffusion probabilistic models for 3d point cloud generation
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Nerf: Representing scenes as neural radiance fields for view synthesis
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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Diffrf: Rendering-guided 3d radiance field diffusion
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Learning to learn with generative models of neural network checkpoints
William Peebles, Ilija Radosavovic, Tim Brooks, Alexei A Efros, and Jitendra Malik · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
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High-resolution image synthesis with latent diffusion models
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Photorealistic text-to-image diffusion models with deep language understanding
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Stylegan-xl: Scaling stylegan to large diverse datasets
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Texturify: Generating textures on 3d shape surfaces
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Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano · 2022
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Lion: Latent point diffusion models for 3d shape generation
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Generative multiplane images: Making a 2d gan 3d-aware
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Text-to-4d dynamic scene generation
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3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models
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