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Generative models aim to learn the distribution of observed data by generating new instances.
J. T. Kajiya and B. P. Von Herzen, “Ray tracing volume densities,” SIGGRAPH , 1984
1984
Earlier work this paper cites.
W. E. Lorensen and H. E. Cline, “Marching cubes: A high resolution 3d surface construction algorithm,” SIGGRAPH , 1987
1987
Earlier work this paper cites.
J. C. Hart, “Sphere tracing: A geometric method for the antialiased ray tracing of implicit surfaces,” The Visual Computer , 1996
1996
Earlier work this paper cites.
Y. Bengio, R. Ducharme, and P. Vincent, “A neural probabilistic language model,” NeurIPS , 2000
2000
Earlier work this paper cites.
G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural computation , 2002
2002
Earlier work this paper cites.
A. Yuille and D. Kersten, “Vision as bayesian inference: analysis by synthesis?” Trends in Cognitive Sciences , 2006
2006
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NeurIPS , 2012
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in NeurIPS , 2014
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in ICLR , 2014
2014
Earlier work this paper cites.
M. M. Loper and M. J. Black, “Opendr: An approximate differentiable renderer,” in ECCV , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , 2015
2015
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in ICCV , 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” NeurIPS , 2015
2015
Earlier work this paper cites.
D. Rezende and S. Mohamed, “Variational inference with normalizing flows,” in ICML , 2015
2015
Earlier work this paper cites.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” in CVPR , 2015
2015
Earlier work this paper cites.
D. Maturana and S. Scherer, “Voxnet: A 3d convolutional neural network for real-time object recognition,” in IROS , 2015
2015
Earlier work this paper cites.
J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum, “Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling,” in NeurIPS , 2016
2016
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” in ICLR , 2016
2016
Earlier work this paper cites.
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese, “3d-r2n2: A unified approach for single and multi-view 3d object reconstruction,” in ECCV , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther, “Autoencoding beyond pixels using a learned similarity metric,” in ICML , 2016
2016
Earlier work this paper cites.
X. Wang and A. Gupta, “Generative image modeling using style and structure adversarial networks,” in ECCV , 2016
2016
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV , 2017
2017
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in CVPR , 2017
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in CVPR , 2017
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” NeurIPS , 2017
2017
Earlier work this paper cites.
A. Sinha, A. Unmesh, Q. Huang, and K. Ramani, “Surfnet: Generating 3d shape surfaces using deep residual networks,” in CVPR , 2017
2017
Earlier work this paper cites.
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe, “Variational inference: A review for statisticians,” J. of the American statis. Associat. , 2017
2017
Earlier work this paper cites.
C. Häne, S. Tulsiani, and J. Malik, “Hierarchical surface prediction for 3d object reconstruction,” in 3DV , 2017
2017
Earlier work this paper cites.
M. Tatarchenko, A. Dosovitskiy, and T. Brox, “Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs,” in ICCV , 2017
2017
Earlier work this paper cites.
G. Riegler, A. Osman Ulusoy, and A. Geiger, “Octnet: Learning deep 3d representations at high resolutions,” in CVPR , 2017
2017
Earlier work this paper cites.
P.-S. Wang, Y. Liu, Y.-X. Guo, C.-Y. Sun, and X. Tong, “O-cnn: Octree-based convolutional neural networks for 3d shape analysis,” TOG , 2017
2017
Earlier work this paper cites.
H. Fan, H. Su, and L. J. Guibas, “A point set generation network for 3d object reconstruction from a single image,” in CVPR , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Liu, F. Yu, and T. A. Funkhouser, “Interactive 3d modeling with a generative adversarial network,” in 3DV , 2017
2017
Earlier work this paper cites.
D. Xu, D. Anguelov, and A. Jain, “Pointfusion: Deep sensor fusion for 3d bounding box estimation,” in CVPR , 2018
2018
Earlier work this paper cites.
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3d point clouds,” in ICML , 2018
2018
Earlier work this paper cites.
N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, and Y.-G. Jiang, “Pixel2mesh: Generating 3d mesh models from single rgb images,” in ECCV , 2018
2018
Earlier work this paper cites.
T. Zhou, R. Tucker, J. Flynn, G. Fyffe, and N. Snavely, “Stereo magnification: Learning view synthesis using multiplane images,” in SIGGRAPH , 2018
2018
Earlier work this paper cites.
A. Oussidi and A. Elhassouny, “Deep generative models: Survey,” in ISCV , 2018
2018
Earlier work this paper cites.
M. Zollhöfer, P. Stotko, A. Görlitz, C. Theobalt, M. Nießner, R. Klein, and A. Kolb, “State of the art on 3d reconstruction with rgb-d cameras,” in CGF , 2018
2018
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in ICLR , 2018
2018
Earlier work this paper cites.
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on x-transformed points,” NeurIPS , 2018
2018
Earlier work this paper cites.
N. Verma, E. Boyer, and J. Verbeek, “Feastnet: Feature-steered graph convolutions for 3d shape analysis,” in CVPR , 2018
2018
Earlier work this paper cites.
M. Fey, J. E. Lenssen, F. Weichert, and H. Müller, “Splinecnn: Fast geometric deep learning with continuous b-spline kernels,” in CVPR , 2018
2018
Earlier work this paper cites.
A. Kanazawa, S. Tulsiani, A. A. Efros, and J. Malik, “Learning category-specific mesh reconstruction from image collections,” in ECCV , 2018
2018
Earlier work this paper cites.
H. Kato, Y. Ushiku, and T. Harada, “Neural 3d mesh renderer,” in CVPR , 2018
2018
Earlier work this paper cites.
H. Ben-Hamu, H. Maron, I. Kezurer, G. Avineri, and Y. Lipman, “Multi-chart generative surface modeling,” TOG , 2018
2018
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in CVPR , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Valsesia, G. Fracastoro, and E. Magli, “Learning localized generative models for 3d point clouds via graph convolution,” in ICLR , 2018
2018
Earlier work this paper cites.
J.-Y. Zhu, Z. Zhang, C. Zhang, J. Wu, A. Torralba, J. Tenenbaum, and B. Freeman, “Visual object networks: Image generation with disentangled 3d representations,” NeurIPS , 2018
2018
Earlier work this paper cites.
E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville, “Film: Visual reasoning with a general conditioning layer,” in AAAI , 2018
2018
Earlier work this paper cites.
V. Dumoulin, E. Perez, N. Schucher, F. Strub, H. d. Vries, A. Courville, and Y. Bengio, “Feature-wise transformations,” Distill , 2018
2018
Earlier work this paper cites.
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan et al. , “Argoverse: 3d tracking and forecasting with rich maps,” in CVPR , 2019
2019
Earlier work this paper cites.
Z. Chen and H. Zhang, “Learning implicit fields for generative shape modeling,” in CVPR , 2019
2019
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in CVPR , 2019
2019
Earlier work this paper cites.
V. Sitzmann, J. Thies, F. Heide, M. Nießner, G. Wetzstein, and M. Zollhofer, “Deepvoxels: Learning persistent 3d feature embeddings,” in CVPR , 2019
2019
Earlier work this paper cites.
T. Nguyen-Phuoc, C. Li, L. Theis, C. Richardt, and Y.-L. Yang, “Hologan: Unsupervised learning of 3d representations from natural images,” in ICCV , 2019
2019
Earlier work this paper cites.
S. Lombardi, T. Simon, J. Saragih, G. Schwartz, A. Lehrmann, and Y. Sheikh, “Neural volumes: Learning dynamic renderable volumes from images,” TOG , 2019
2019
Earlier work this paper cites.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” TOG , 2019
2019
Earlier work this paper cites.
M. Meshry, D. B. Goldman, S. Khamis, H. Hoppe, R. Pandey, N. Snavely, and R. Martin-Brualla, “Neural rerendering in the wild,” in CVPR , 2019
2019
Earlier work this paper cites.
W. Yifan, F. Serena, S. Wu, C. Öztireli, and O. Sorkine-Hornung, “Differentiable surface splatting for point-based geometry processing,” TOG , 2019
2019
Earlier work this paper cites.
J. Huang, H. Zhang, L. Yi, T. Funkhouser, M. Nießner, and L. J. Guibas, “Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes,” in CVPR , 2019
2019
Earlier work this paper cites.
R. Hanocka, A. Hertz, N. Fish, R. Giryes, S. Fleishman, and D. Cohen-Or, “Meshcnn: a network with an edge,” TOG , 2019
2019
Earlier work this paper cites.
C. Wen, Y. Zhang, Z. Li, and Y. Fu, “Pixel2mesh++: Multi-view 3d mesh generation via deformation,” in ICCV , 2019
2019
Earlier work this paper cites.
S. Liu, T. Li, W. Chen, and H. Li, “Soft rasterizer: A differentiable renderer for image-based 3d reasoning,” in ICCV , 2019
2019
Earlier work this paper cites.
J. Thies, M. Zollhöfer, and M. Nießner, “Deferred neural rendering: Image synthesis using neural textures,” TOG , 2019
2019
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in NeurIPS , 2019
2019
Earlier work this paper cites.
G. Yang, X. Huang, Z. Hao, M.-Y. Liu, S. Belongie, and B. Hariharan, “Pointflow: 3d point cloud generation with continuous normalizing flows,” in ICCV , 2019
2019
Earlier work this paper cites.
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, “Occupancy networks: Learning 3d reconstruction in function space,” in CVPR , 2019
2019
Earlier work this paper cites.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in CVPR , 2019
2019
Earlier work this paper cites.
H.-Y. Meng, L. Gao, Y.-K. Lai, and D. Manocha, “Vv-net: Voxel vae net with group convolutions for point cloud segmentation,” in ICCV , 2019
2019
Earlier work this paper cites.
Z. Wu, X. Wang, D. Lin, D. Lischinski, D. Cohen-Or, and H. Huang, “Sagnet: Structure-aware generative network for 3d-shape modeling,” TOG , 2019
2019
Earlier work this paper cites.
D. W. Shu, S. W. Park, and J. Kwon, “3d point cloud generative adversarial network based on tree structured graph convolutions,” in ICCV , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
L. Gao, J. Yang, T. Wu, Y.-J. Yuan, H. Fu, Y.-K. Lai, and H. Zhang, “Sdm-net: Deep generative network for structured deformable mesh,” TOG , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
P. Henzler, N. J. Mitra, and T. Ritschel, “Escaping plato’s cave: 3d shape from adversarial rendering,” in ICCV , 2019
2019
Cited alongside, same era.
V. Sitzmann, M. Zollhöfer, and G. Wetzstein, “Scene representation networks: Continuous 3d-structure-aware neural scene representations,” in NeurIPS , 2019
2019
Cited alongside, same era.
R. Abdal, Y. Qin, and P. Wonka, “Image2stylegan: How to embed images into the stylegan latent space?” in ICCV , 2019
2019
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in ECCV , 2020
2020
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” NeurIPS , 2020
2020
Cited alongside, same era.
M. Ibing, I. Lim, and L. Kobbelt, “3d shape generation with grid-based implicit functions,” in CVPR , 2021
2021
Later among the works it cites.
A. Luo, T. Li, W.-H. Zhang, and T. S. Lee, “Surfgen: Adversarial 3d shape synthesis with explicit surface discriminators,” in ICCV , 2021
2021
Later among the works it cites.
L. Gao, T. Wu, Y.-J. Yuan, M.-X. Lin, Y.-K. Lai, and H. Zhang, “Tm-net: Deep generative networks for textured meshes,” TOG , 2021
2021
Later among the works it cites.
X. Chen, D. Cohen-Or, B. Chen, and N. J. Mitra, “Towards a neural graphics pipeline for controllable image generation,” in CGF , 2021
2021
Later among the works it cites.
2021
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R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann, “Shortcut learning in deep neural networks,” Nature Machine Intell. , 2020
2020
Cited alongside, same era.
Y. Shen, C. Yang, X. Tang, and B. Zhou, “Interfacegan: Interpreting the disentangled face representation learned by gans,” TPAMI , 2020
2020
Cited alongside, same era.
K. Schwarz, Y. Liao, M. Niemeyer, and A. Geiger, “Graf: Generative radiance fields for 3d-aware image synthesis,” NeurIPS , 2020
2020
Cited alongside, same era.
Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep learning for 3d point clouds: A survey,” TPAMI , 2020
2020
Cited alongside, same era.
S. Chaudhuri, D. Ritchie, J. Wu, K. Xu, and H. Zhang, “Learning generative models of 3d structures,” in CGF , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of StyleGAN,” in CVPR , 2020
2020
Cited alongside, same era.
Later among the works it cites.
T. DeVries, M. A. Bautista, N. Srivastava, G. W. Taylor, and J. M. Susskind, “Unconstrained scene generation with locally conditioned radiance fields,” in ICCV , 2021
2021
Later among the works it cites.
A. R. Kosiorek, H. Strathmann, D. Zoran, P. Moreno, R. Schneider, S. Mokrá, and D. J. Rezende, “Nerf-vae: A geometry aware 3d scene generative model,” in ICML , 2021
2021
Later among the works it cites.
C. Yang, Y. Shen, and B. Zhou, “Semantic hierarchy emerges in deep generative representations for scene synthesis,” IJCV , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Yuan, Y. Lai, T. Wu, L. Gao, and L. Liu, “A revisit of shape editing techniques: From the geometric to the neural viewpoint,” JCST , 2021
2021
Later among the works it cites.
Z. Zheng, T. Yu, Q. Dai, and Y. Liu, “Deep implicit templates for 3d shape representation,” in CVPR , 2021
2021
Later among the works it cites.
T. Jahan, Y. Guan, and O. van Kaick, “Semantics-guided latent space exploration for shape generation,” CGF , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Eckart, W. Yuan, C. Liu, and J. Kautz, “Self-supervised learning on 3d point clouds by learning discrete generative models,” in CVPR , 2021
2021
Later among the works it cites.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
A. Tewari, J. Thies, B. Mildenhall, P. Srinivasan, E. Tretschk, W. Yifan, C. Lassner, V. Sitzmann, R. Martin-Brualla, S. Lombardi et al. , “Advances in neural rendering,” in CGF , 2022
2022
Closest in time.
2022
Closest in time.
Y. Xie, T. Takikawa, S. Saito, O. Litany, S. Yan, N. Khan, F. Tombari, J. Tompkin, V. Sitzmann, and S. Sridhar, “Neural fields in visual computing and beyond,” in CGF , 2022
2022
Closest in time.
A. Jain, B. Mildenhall, J. T. Barron, P. Abbeel, and B. Poole, “Zero-shot text-guided object generation with dream fields,” in CVPR , 2022
2022
Closest in time.
2022
Closest in time.
Y. Xu, S. Peng, C. Yang, Y. Shen, and B. Zhou, “3d-aware image synthesis via learning structural and textural representations,” in CVPR , 2022
2022
Closest in time.
K. Schwarz, A. Sauer, M. Niemeyer, Y. Liao, and A. Geiger, “Voxgraf: Fast 3d-aware image synthesis with sparse voxel grids,” in NeurIPS , 2022
2022
Closest in time.
P. Mittal, Y.-C. Cheng, M. Singh, and S. Tulsiani, “Autosdf: Shape priors for 3d completion, reconstruction and generation,” in CVPR , 2022
2022
Closest in time.
D. Rückert, L. Franke, and M. Stamminger, “Adop: Approximate differentiable one-pixel point rendering,” TOG , 2022
2022
Closest in time.
X. Chen, T. Jiang, J. Song, J. Yang, M. J. Black, A. Geiger, and O. Hilliges, “gdna: Towards generative detailed neural avatars,” in CVPR , 2022
2022
Closest in time.
Z. Shi, Y. Shen, J. Zhu, D.-Y. Yeung, and Q. Chen, “3d-aware indoor scene synthesis with depth priors,” in ECCV , 2022
2022
Closest in time.
J. Sun, X. Wang, Y. Zhang, X. Li, Q. Zhang, Y. Liu, and J. Wang, “Fenerf: Face editing in neural radiance fields,” in CVPR , 2022
2022
Closest in time.
J. Gu, L. Liu, P. Wang, and C. Theobalt, “Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis,” in ICLR , 2022
2022
Closest in time.
Y. Deng, J. Yang, J. Xiang, and X. Tong, “Gram: Generative radiance manifolds for 3d-aware image generation,” in CVPR , 2022
2022
Closest in time.
Y. Xue, Y. Li, K. K. Singh, and Y. J. Lee, “Giraffe hd: A high-resolution 3d-aware generative model,” in CVPR , 2022
2022
Closest in time.
E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis et al. , “Efficient geometry-aware 3d generative adversarial networks,” in CVPR , 2022
2022
Closest in time.
S. Fridovich-Keil, A. Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa, “Plenoxels: Radiance fields without neural networks,” in CVPR , 2022
2022
Closest in time.
Q. Xu, Z. Xu, J. Philip, S. Bi, Z. Shu, K. Sunkavalli, and U. Neumann, “Point-nerf: Point-based neural radiance fields,” in CVPR , 2022
2022
Closest in time.
2022
Closest in time.
B. Yang, C. Bao, J. Zeng, H. Bao, Y. Zhang, Z. Cui, and G. Zhang, “Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing,” in ECCV , 2022
2022
Closest in time.
J. Gao, T. Shen, Z. Wang, W. Chen, K. Yin, D. Li, O. Litany, Z. Gojcic, and S. Fidler, “GET3D: A generative model of high quality 3d textured shapes learned from images,” in NeurIPS , 2022
2022
Closest in time.
X. Zeng, A. Vahdat, F. Williams, Z. Gojcic, O. Litany, S. Fidler, and K. Kreis, “Lion: Latent point diffusion models for 3d shape generation,” in NeurIPS , 2022
2022
Closest in time.
2022
Closest in time.
X. Chen, T. Jiang, J. Song, J. Yang, M. J. Black, A. Geiger, and O. Hilliges, “gdna: Towards generative detailed neural avatars,” in CVPR , 2022
2022
Closest in time.
E. Dupont, H. Kim, S. Eslami, D. Rezende, and D. Rosenbaum, “From data to functa: Your data point is a function and you should treat it like one,” in ICML , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
R. Or-El, X. Luo, M. Shan, E. Shechtman, J. J. Park, and I. Kemelmacher-Shlizerman, “Stylesdf: High-resolution 3d-consistent image and geometry generation,” in CVPR , 2022
2022
Closest in time.
X. Zhang, Z. Zheng, D. Gao, B. Zhang, P. Pan, and Y. Yang, “Multi-view consistent generative adversarial networks for 3d-aware image synthesis,” in CVPR , 2022
2022
Closest in time.
Z. Shi, Y. Xu, Y. Shen, D. Zhao, Q. Chen, and D.-Y. Yeung, “Improving 3d-aware image synthesis with a geometry-aware discriminator,” in NeurIPS , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
S. Cai, A. Obukhov, D. Dai, and L. Van Gool, “Pix2nerf: Unsupervised conditional p-gan for single image to neural radiance fields translation,” in CVPR , 2022
2022
Closest in time.
A. Tewari, X. Pan, O. Fried, M. Agrawala, C. Theobalt et al. , “Disentangled3d: Learning a 3d generative model with disentangled geometry and appearance from monocular images,” in CVPR , 2022
2022
Closest in time.
2022
Closest in time.
D. Rebain, M. J. Matthews, K. M. Yi, D. Lagun, and A. Tagliasacchi, “Lolnerf: Learn from one look,” in CVPR , 2022
2022
Closest in time.
F. Tan, S. Fanello, A. Meka, S. Orts-Escolano, D. Tang, R. Pandey, J. Taylor, P. Tan, and Y. Zhang, “Volux-gan: A generative model for 3d face synthesis with hdri relighting,” TOG , 2022
2022
Closest in time.
J. Zhang, E. Sangineto, H. Tang, A. Siarohin, Z. Zhong, N. Sebe, and W. Wang, “3d-aware semantic-guided generative model for human synthesis,” in ECCV , 2022
2022
Closest in time.
J. Sun, X. Wang, Y. Shi, L. Wang, J. Wang, and Y. Liu, “Ide-3d: Interactive disentangled editing for high-resolution 3d-aware portrait synthesis,” in SIGGRAPH Asia , 2022
2022
Closest in time.
M. A. Bautista, P. Guo, S. Abnar, W. Talbott, A. Toshev, Z. Chen, L. Dinh, S. Zhai, H. Goh, D. Ulbricht et al. , “Gaudi: A neural architect for immersive 3d scene generation,” NeurIPS , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Z. Liu, Y. Wang, X. Qi, and C. Fu, “Towards implicit text-guided 3d shape generation,” CVPR , 2022
2022
Closest in time.
2022
Closest in time.
A. Noguchi, X. Sun, S. Lin, and T. Harada, “Unsupervised learning of efficient geometry-aware neural articulated representations,” in ECCV , 2022
2022
Closest in time.
C. Wang, M. Chai, M. He, D. Chen, and J. Liao, “Clip-nerf: Text-and-image driven manipulation of neural radiance fields,” CVPR , 2022
2022
Closest in time.
2022
Closest in time.
J. Guo, F. Zhong, R. Xiong, Y. Liu, Y. Wang, and Y. Liao, “A visual navigation perspective for category-level object pose estimation,” in ECCV , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
C.-H. Lin, J. Gao, L. Tang, T. Takikawa, X. Zeng, X. Huang, K. Kreis, S. Fidler, M.-Y. Liu, and T.-Y. Lin, “Magic3d: High-resolution text-to-3d content creation,” in CVPR , 2023
2023
Closest in time.
N. Müller, Y. Siddiqui, L. Porzi, S. R. Bulo, P. Kontschieder, and M. Nießner, “Diffrf: Rendering-guided 3d radiance field diffusion,” in CVPR , 2023
2023
Closest in time.
Z. Liu, Y. Feng, M. J. Black, D. Nowrouzezahrai, L. Paull, and W. Liu, “Meshdiffusion: Score-based generative 3d mesh modeling,” in ICLR , 2023
2023
Closest in time.
Z. Lyu, J. Wang, Y. An, Y. Zhang, D. Lin, and B. Dai, “Controllable mesh generation through sparse latent point diffusion models,” in CVPR , 2023
2023
Closest in time.
Z. Shi, Y. Shen, Y. Xu, S. Peng, Y. Liao, S. Guo, Q. Chen, and D.-Y. Yeung, “Learning 3d-aware image synthesis with unknown pose distribution,” in CVPR , 2023
2023
Closest in time.
Y. Xu, M. Chai, Z. Shi, S. Peng, I. Skorokhodov, A. Siarohin, C. Yang, Y. Shen, H.-Y. Lee, B. Zhou et al. , “Discoscene: Spatially disentangled generative radiance fields for controllable 3d-aware scene synthesis,” in CVPR , 2023
2023
Closest in time.
J. Sun, X. Wang, L. Wang, X. Li, Y. Zhang, H. Zhang, and Y. Liu, “Next3d: Generative neural texture rasterization for 3d-aware head avatars,” in CVPR , 2023
2023
Closest in time.
2023
Closest in time.