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Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution.
Gower JC (1975) Generalized procrustes analysis. Psychometrika 40(1):33–51
1975
Earlier work this paper cites.
Besl PJ, McKay ND (1992) Method for registration of 3-d shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol 1611, pp 586–607
1992
Earlier work this paper cites.
Davies R, Twining C, Taylor C (2008) Statistical models of shape: Optimization and evaluation. Springer Science & Business Media
2008
Earlier work this paper cites.
De Smet M, Van Gool L (2010) Optimal regions for linear model-based 3d face reconstruction. In: Proceedings of the Asian Conference on Computer Vision, pp 276–289
2010
Earlier work this paper cites.
Jolliffe I (2011) Principal component analysis. In: International Encyclopedia of Statistical Science, Springer, pp 1094–1096
2011
Earlier work this paper cites.
Newcombe RA, Izadi S, Hilliges O, Molyneaux D, Kim D, Davison AJ, Kohi P, Shotton J, Hodges S, Fitzgibbon A (2011) Kinectfusion: Real-time dense surface mapping and tracking. In: Proceedings of the IEEE international symposium on Mixed and Augmented Reality (ISMAR), pp 127–136
2011
Earlier work this paper cites.
Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:13126114
2013
Earlier work this paper cites.
Booth J, Zafeiriou S (2014) Optimal uv spaces for facial morphable model construction. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp 4672–4676
2014
Earlier work this paper cites.
Brunton A, Salazar A, Bolkart T, Wuhrer S (2014) Review of statistical shape spaces for 3d data with comparative analysis for human faces. Computer Vision and Image Understanding 128:1–17
2014
Earlier work this paper cites.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of the Advances in neural information processing systems, pp 2672–2680
2014
Earlier work this paper cites.
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980
2014
Earlier work this paper cites.
Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:14111784
2014
Earlier work this paper cites.
Clevert DA, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:151107289
2015
Earlier work this paper cites.
Mahendran A, Vedaldi A (2015) Understanding deep image representations by inverting them. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5188–5196
2015
Earlier work this paper cites.
Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:151106434
2015
Earlier work this paper cites.
Booth J, Roussos A, Zafeiriou S, Ponniah A, Dunaway D (2016) A 3d morphable model learnt from 10,000 faces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5543–5552
2016
Earlier work this paper cites.
Dosovitskiy A, Brox T (2016) Generating images with perceptual similarity metrics based on deep networks. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp 658–666
2016
Earlier work this paper cites.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
2016
Cited alongside, same era.
Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of the European Conference on Computer Vision, Springer, pp 694–711
2016
Cited alongside, same era.
Zhao J, Mathieu M, LeCun Y (2016) Energy-based generative adversarial network. arXiv preprint arXiv:160903126
2016
Cited alongside, same era.
Berthelot D, Schumm T, Metz L (2017) Began: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:170310717
2017
Cited alongside, same era.
Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol 1, p 7
Maron H, Galun M, Aigerman N, Trope M, Dym N, Yumer E, Kim VG, Lipman Y (2017) Convolutional neural networks on surfaces via seamless toric covers. ACM Transactions on Graphics 36(4):71
2017
Later among the works it cites.
Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1(2):4
2017
Later among the works it cites.
Richardson E, Sela M, Or-El R, Kimmel R (2017) Learning detailed face reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5553–5562
2017
Later among the works it cites.
Tran AT, Hassner T, Masi I, Medioni G (2017) Regressing robust and discriminative 3d morphable models with a very deep neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1493–1502
2017
Later among the works it cites.
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2017
Cited alongside, same era.
Bronstein MM, Bruna J, LeCun Y, Szlam A, Vandergheynst P (2017) Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine 34(4):18–42
2017
Cited alongside, same era.
Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J (2017) Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. arXiv preprint 1711
2017
Cited alongside, same era.
Dou P, Shah SK, Kakadiaris IA (2017) End-to-end 3d face reconstruction with deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 21–26
2017
Cited alongside, same era.
Fan H, Su H, Guibas LJ (2017) A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol 2, p 6
2017
Cited alongside, same era.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol 1, p 3
2017
Cited alongside, same era.
Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. arXiv preprint
2017
Cited alongside, same era.
Jackson AS, Bulat A, Argyriou V, Tzimiropoulos G (2017) Large pose 3d face reconstruction from a single image via direct volumetric cnn regression. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 1031–1039
2017
Cited alongside, same era.
Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), vol 1, p 4
2017
Later among the works it cites.
Wang W, Huang Q, You S, Yang C, Neumann U (2017) Shape inpainting using 3d generative adversarial network and recurrent convolutional networks. arXiv preprint arXiv:171106375
2017
Later among the works it cites.
Yang C, Lu X, Lin Z, Shechtman E, Wang O, Li H (2017) High-resolution image inpainting using multi-scale neural patch synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol 1, p 3
2017
Later among the works it cites.
Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint
2017
Later among the works it cites.
Cheng S, Kotsia I, Pantic M, Zafeiriou S (2018) 4dfab: A large scale 4d database for facial expression analysis and biometric applications. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 5117–5126
2018
Later among the works it cites.
Feng Y, Wu F, Shao X, Wang Y, Zhou X (2018) Joint 3d face reconstruction and dense alignment with position map regression network. arXiv preprint arXiv:180307835
2018
Later among the works it cites.
Genova K, Cole F, Maschinot A, Sarna A, Vlasic D, Freeman WT (2018) Unsupervised training for 3d morphable model regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 8377–8386
2018
Later among the works it cites.
Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation. Proceedings of the International Conference for Learning Representations (ICLR)
2018
Later among the works it cites.
Lucic M, Kurach K, Michalski M, Gelly S, Bousquet O (2018) Are gans created equal? a large-scale study. Proceedings of the Advances in Neural Information Processing Systems (NIPS)
2018
Later among the works it cites.
Nguyen K, Fookes C, Sridharan S, Tistarelli M, Nixon M (2018) Super-resolution for biometrics: A comprehensive survey. Pattern Recognition 78:23–42
2018
Later among the works it cites.
Ranjan A, Bolkart T, Sanyal S, Black MJ (2018) Generating 3d faces using convolutional mesh autoencoders. arXiv preprint arXiv:180710267
2018
Later among the works it cites.
Wang TC, Liu MY, Zhu JY, Tao A, Kautz J, Catanzaro B (2018) High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol 1, p 5
2018
Later among the works it cites.