The DOI matches its Crossref record; 261 of the 287 references it cites were found in a registry or an index.
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
Open accessAbstract
The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner. It has enabled the generation of high-resolution photorealistic images and videos, a task that was challenging or impossible with prior methods. It has also led to the creation of many new applications in content creation. In this article, we provide an overview of GANs with a special focus on algorithms and applications for visual synthesis. We cover several important techniques to stabilize GAN training, which has a reputation for being notoriously difficult. We also discuss its applications to image translation, image processing, video synthesis, and neural rendering.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It is becoming increasingly simple to use pretrained GAN to instantly replace one person's face in an image or video with the face of another person (Liu etal. 2021b).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Liu et al. 2021b
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2662206 · Cited by 8,983
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365
Enhanced Deep Residual Networks for Single Image Super-Resolution
Lim, B. O., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.151 · Cited by 8,117
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870 · cited 2×
“Deep learning has enabled faster and more accurate SR methods, including SRCNN [42], FSRCNN [43], ESPCN [182], VDSR [88], SRResNet [102], EDSR [111], SRDenseNet [203], MemNet [193], RDN [247], WDSR [236], and many others.”
MethodShi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.207 · Cited by 7,570
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_18 · Cited by 6,003
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., & Loy, C. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_5 · Cited by 5,241
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
Huang, X., & Belongie, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.167 · Cited by 5,076
Self-citationWaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017) · arXiv · doi:10.48550/arxiv.1710.10196 · Cited by 4,727
A morphable model for the synthesis of 3D faces
Blanz, V., & Vetter, T. R. (1999) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/311535.311556 · Cited by 4,695
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., & Catanzaro, B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00917 · Cited by 4,622
Self-citationGenerative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540
Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477
Residual Dense Network for Image Super-Resolution
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00262 · Cited by 4,371
Instance Normalization: The Missing Ingredient for Fast StylizationPreprint
Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.08022 · Cited by 4,325
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00916 · Cited by 4,163
Stochastic Backpropagation and Approximate Inference in Deep Generative ModelsPreprint
Rezende, D. J., Mohamed, S., & Wierstra, D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.4082 · Cited by 3,921
Accelerating the Super-Resolution Convolutional Neural Network
Dong, C., Loy, C. C., & Tang, X. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_25 · Cited by 3,786
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734 · cited 2×
“[67] combine the SMPL models [116] with the implicit field estimation framework [163] to rig the reconstructed meshes with desired motions.”
MethodLarge Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717
Wu, Y., & He, K. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01198-w · Cited by 3,601
Spectral Normalization for Generative Adversarial Networks
Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018) · arXiv · doi:10.48550/arxiv.1802.05957 · Cited by 3,438
Self-Attention Generative Adversarial Networks
Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2018) · arXiv · doi:10.48550/arxiv.1805.08318 · Cited by 3,353
Conditional Image Synthesis With Auxiliary Classifier GANsPreprint
Odena, A., Olah, C., & Shlens, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09585 · Cited by 3,030
Semantic Image Synthesis With Spatially-Adaptive Normalization
Park, T., Liu, M.-Y., Wang, T.-C., & Zhu, J.-Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00244 · Cited by 2,974
Self-citationBarnes, C., Shechtman, E., Finkelstein, A., & Goldman, D. B. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531330 · Cited by 2,888
Multimodal Unsupervised Image-to-Image Translation
Huang, X., Liu, M.-Y., Belongie, S., & Kautz, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_11 · Cited by 2,710
Self-citationGenerative Image Inpainting with Contextual Attention
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., & Huang, T. S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00577 · Cited by 2,633
Self-citationAn Introduction to Variational Autoencoders
Kingma, D. P., & Welling, M. (2019) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000056 · Cited by 2,609
Variational Inference with Normalizing FlowsPreprint
Rezende, D. J., & Mohamed, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05770 · Cited by 2,583
Generative Adversarial Networks
Drori, I. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108891530.013 · Cited by 2,557
Image Inpainting for Irregular Holes Using Partial Convolutions
Liu, G., Reda, F. A., Shih, K. J., Wang, T.-C., Tao, A., & Catanzaro, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_6 · Cited by 2,419 · cited 2×
“Recently, deep learning and GANbased approaches [73], [78], [114], [145], [222], [229], [237], [238], [241], [249] open a new direction for image inpainting using deep neural networks learned on largescale data in an end-to-end fashion.”
BackgroundMethodSelf-citationDualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Yi, Z., Zhang, H., Tan, P., & Gong, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.310 · Cited by 2,262
Arjovsky, M., Chintala, S., & Bottou, L. (2017) · arXiv · doi:10.48550/arxiv.1701.07875 · Cited by 2,225
Globally and locally consistent image completion
Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073659 · Cited by 2,203
Unsupervised Image-to-Image Translation NetworksPreprint
Liu, M.-Y., Breuel, T. M., & Kautz, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00848 · Cited by 2,194
Self-citationFree-Form Image Inpainting With Gated Convolution
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., & Huang, T. S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00457 · Cited by 2,151
Self-citationDensity estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016) · arXiv · Cited by 2,067
NIPS 2016 Tutorial: Generative Adversarial NetworksPreprint
Goodfellow, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00160 · Cited by 2,048
Conditional Image Generation with PixelCNN DecodersPreprint
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.05328 · Cited by 2,032
MemNet: A Persistent Memory Network for Image Restoration
Tai, Y. Y., Yang, J. J., Liu, X., & Xu, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.486 · Cited by 1,954
Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890
Unsupervised Learning of Video Representations using LSTMs
Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., & Matas, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00854 · Cited by 1,833
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Niessner, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.262 · Cited by 1,747
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Kim, T., Cha, M., Kim, H., Lee, J. K., & Kim, J. (2017) · arXiv · doi:10.48550/arxiv.1703.05192 · Cited by 1,716
Contrastive Learning for Unpaired Image-to-Image Translation
Park, T., Efros, A. A., Zhang, R., & Zhu, J.-Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_19 · Cited by 1,663
Deep multi-scale video prediction beyond mean square errorPreprint
Mathieu, M., Couprie, C., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05440 · Cited by 1,592
NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1410.8516 · Cited by 1,493
Coupled Generative Adversarial NetworksPreprint
Liu, M.-Y., & Tuzel, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.07536 · Cited by 1,445
Self-citationThies, J., Zollhöfer, M., & Nießner, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323035 · Cited by 1,443
Image Super-Resolution Using Dense Skip Connections
Tong, T., Li, G., Liu, X., & Gao, Q. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.514 · Cited by 1,425
DensePose: Dense Human Pose Estimation in the Wild.
Güler, R. A., Neverova, N., & Kokkinos, I. (2018) · UCL Discovery (University College London) · Cited by 1,421
Autoencoding beyond pixels using a learned similarity metric
Larsen, A., Sønderby, S. K., Larochelle, H., & Winther, O. (2016) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 1,317
Diverse Image-to-Image Translation via Disentangled Representations
Lee, H.-Y., Tseng, H.-Y., Huang, J., Singh, M., & Yang, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_3 · Cited by 1,300
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
Saito, S., Huang, Z., Natsume, R., Morishima, S., Li, H., & Kanazawa, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00239 · Cited by 1,288
Generative Visual Manipulation on the Natural Image Manifold
Zhu, J.-Y., Krähenbühl, P., Shechtman, E., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_36 · Cited by 1,234
CNN-Generated Images Are Surprisingly Easy to Spot… for Now
Wang, S.-Y., Wang, O., Zhang, R., Owens, A., & Efros, A. A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00872 · Cited by 1,210
Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995) · Neural Computation · doi:10.1162/neco.1995.7.5.889 · Cited by 1,207
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Nowozin, S., Cseke, B., & Tomioka, R. (2016) · arXiv · doi:10.48550/arxiv.1606.00709 · Cited by 1,183
Toward Multimodal Image-to-Image TranslationPreprint
Zhu, J.-Y., Zhang, R., Pathak, D., Darrell, T., Efros, A. A., Wang, O., & Shechtman, E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.11586 · Cited by 1,170
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
Lee, C.-H., Liu, Z., Wu, L., & Luo, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00559 · Cited by 1,130
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Sajjadi, M. S. M., Schölkopf, B., & Hirsch, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.481 · Cited by 1,088
Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
Abdal, R., Qin, Y., & Wonka, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00453 · Cited by 1,085
The Perception-Distortion Tradeoff
Blau, Y., & Michaeli, T. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00652 · Cited by 1,077
MoCoGAN: Decomposing Motion and Content for Video Generation
Tulyakov, S., Liu, M.-Y., Yang, X., & Kautz, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00165 · Cited by 1,076
Self-citationInterpreting the Latent Space of GANs for Semantic Face Editing
Shen, Y., Gu, J., Tang, X., & Zhou, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00926 · Cited by 1,063
Synthesizing Obama: learning lip sync from audio
Suwajanakorn, S., Seitz, S. M., & Kemelmacher-Shlizerman, I. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073640 · Cited by 1,057
Which Training Methods for GANs do actually Converge?Preprint
Mescheder, L., Geiger, A., & Nowozin, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04406 · Cited by 1,029
Generating Videos with Scene DynamicsPreprint
Vondrick, C., Pirsiavash, H., & Torralba, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.02612 · Cited by 976
SinGAN: Learning a Generative Model From a Single Natural Image
Shaham, T. R., Dekel, T., & Michaeli, T. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00467 · Cited by 965
Pros and cons of GAN evaluation measures
Borji, A. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.10.009 · Cited by 963
An Introduction to Variational Autoencoders
Kingma, D. P., & Welling, M. (2019) · doi:10.1561/9781680836233 · Cited by 924
Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningPreprint
Lotter, W. E., Kreiman, G., & Cox, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08104 · Cited by 904
The relativistic discriminator: a key element missing from standard GANPreprint
Jolicoeur‐Martineau, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00734 · Cited by 901
Are GANs Created Equal? A Large-Scale StudyPreprint
Lučić, M., Kurach, K., Michalski, M., Gelly, S., & Bousquet, O. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10337 · Cited by 865
A Tutorial on Energy-Based Learning
LeCun, Y., Chopra, S., Hadsell, R. T., Ranzato, A., & Huang, F. J. (2006) · Cited by 844
EdgeConnect: Generative Image Inpainting with Adversarial Edge LearningPreprint
Nazeri, K., Ng, E., Joseph, T., Qureshi, F. Z., & Ebrahimi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00212 · Cited by 814
Unsupervised Learning for Physical Interaction through Video Prediction
Finn, C., Goodfellow, I., & Levine, S. (2016) · arXiv · doi:10.48550/arxiv.1605.07157 · Cited by 759
Chan, C., Ginosar, S., Zhou, T., & Efros, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00603 · Cited by 737
Pose Guided Person Image GenerationPreprint
Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., & Gool, L. V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.09368 · Cited by 719
Recent Progress on Generative Adversarial Networks (GANs): A Survey
Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2905015 · Cited by 718
FSGAN: Subject Agnostic Face Swapping and Reenactment
Nirkin, Y., Keller, Y., & Hassner, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00728 · Cited by 706
Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Nießner, M., Pérez, P., Richardt, C., Zollhöfer, M., & Theobalt, C. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201283 · Cited by 702
Few-Shot Unsupervised Image-to-Image Translation
Liu, M.-Y., Huang, X., Mallya, A., Karras, T., Aila, T., Lehtinen, J., & Kautz, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01065 · Cited by 687
Self-citationFew-Shot Adversarial Learning of Realistic Neural Talking Head Models
Zakharov, E., Shysheya, A., Burkov, E., & Lempitsky, V. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00955 · Cited by 687
Image Blind Denoising with Generative Adversarial Network Based Noise Modeling
Chen, J., Chen, J., Chao, H., & Yang, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00333 · Cited by 627
Generative Adversarial Networks for Extreme Learned Image Compression
Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., & Gool, L. V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00031 · Cited by 624
GANimation: Anatomically-Aware Facial Animation from a Single Image
Pumarola, A., Agudo, A., Martinez, A. M., Sanfeliu, A., & Moreno-Noguer, F. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_50 · Cited by 621
cGANs with Projection DiscriminatorPreprint
Miyato, T., & Koyama, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.05637 · Cited by 619
The 2018 PIRM Challenge on Perceptual Image Super-Resolution
Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., & Zelnik‐Manor, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_21 · Cited by 609
Learning a no-reference quality metric for single-image super-resolution
Ma, C., Yang, C., Yang, X., & Yang, M. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.12.009 · Cited by 609
An Introduction to Restricted Boltzmann Machines
Fischer, A., & Igel, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33275-3_2 · Cited by 605
Tolstikhin, I., Bousquet, O., Gelly, S., & Schölkopf, B. (2018) · MPG.PuRe (Max Planck Society) · Cited by 602
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Liu, G., Tao, A., Kautz, J., & Catanzaro, B. (2018) · arXiv · doi:10.48550/arxiv.1808.06601 · Cited by 594
Self-citationThies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2016) · ACM SIGGRAPH Emerging Technologies · doi:10.1145/2929464.2929475 · Cited by 589
Unsupervised Learning of Disentangled Representations from VideoPreprint
Denton, E., & Birodkar, V. (2017) · Neural Information Processing Systems · doi:10.48550/arxiv.1705.10915 · Cited by 586
First Order Motion Model for Image Animation
Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., & Sebe, N. (2020) · arXiv · doi:10.48550/arxiv.2003.00196 · Cited by 585
Salimans, T., Karpathy, A., Chen, X., & Kingma, D. P. (2017) · arXiv · doi:10.48550/arxiv.1701.05517 · Cited by 584
Detecting and Simulating Artifacts in GAN Fake Images
Zhang, X., Karaman, S., & Chang, S.-F. (2019) · 2019 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs47025.2019.9035107 · Cited by 567 · cited 1×
“Many other approaches have been proposed to detect GAN-generated images across different generator architectures [204], [233].”
BackgroundFace transfer with multilinear models
Vlasic, D., Brand, M., Pfister, H., & Popović, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073209 · Cited by 565
DeepVoxels: Learning Persistent 3D Feature Embeddings
Sitzmann, V., Thies, J., Heide, F., Nießner, M., Wetzstein, G., & Zollhöfer, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00254 · Cited by 564
Zheng, C., Cham, T., & Cai, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00153 · Cited by 551
Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting
Zeng, Y., Fu, J., Chao, H., & Guo, B. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00158 · Cited by 543
Disentangled Person Image Generation
Ma, L., Sun, Q., Georgoulis, S., Gool, L. V., Schiele, B., & Fritz, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00018 · Cited by 531
Deformable GANs for Pose-Based Human Image Generation
Siarohin, A., Sangineto, E., Lathuilière, S., & Sebe, N. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00359 · Cited by 515
Image2StyleGAN++: How to Edit the Embedded Images?
Abdal, R., Qin, Y., & Wonka, P. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00832 · Cited by 509
One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
Wang, T.-C., Mallya, A., & Liu, M.-Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00991 · Cited by 496
Self-citationDecomposing Motion and Content for Natural Video Sequence Prediction
Villegas, R., Yang, J., Hong, S., Lin, X., & Lee, H. (2017) · arXiv · doi:10.48550/arxiv.1706.08033 · Cited by 488
State of the Art on Neural Rendering
Tewari, A., Fried, O., Thies, J., Sitzmann, V., Lombardi, S., Sunkavalli, K., Martin-Brualla, R., Simon, T., Saragih, J., Nießner, M., et al. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14022 · Cited by 480
An Uncertain Future: Forecasting from Static Images Using Variational Autoencoders
Walker, J., Doersch, C., Gupta, A., & Hebert, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_51 · Cited by 475
A Variational U-Net for Conditional Appearance and Shape Generation
Esser, P., & Sutter, E. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00923 · Cited by 463
GAN Dissection: Visualizing and Understanding Generative Adversarial NetworksPreprint
Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10597 · Cited by 455
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Mao, Q., Lee, H.-Y., Tseng, H.-Y., Ma, S., & Yang, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00152 · Cited by 445
Generating Videos with Scene Dynamics
Vondrick, C., Pirsiavash, H., & Torralba, A. (2016) · Maryland Shared Open Access Repository · doi:10.13016/m26gih-tnyz · Cited by 441
Stochastic Adversarial Video PredictionPreprint
Lee, A. X., Zhang, R., Ebert, F., Abbeel, P., Finn, C., & Levine, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.01523 · Cited by 440
Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss
Chen, L., Maddox, R. K., Duan, Z., & Xu, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00802 · Cited by 437
X2Face: A Network for Controlling Face Generation Using Images, Audio, and Pose Codes
Wiles, O., Koepke, A. S., & Zisserman, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_41 · Cited by 431
Learning Blind Video Temporal Consistency
Lai, W., Huang, J., Wang, O., Shechtman, E., Yumer, E., & Yang, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_11 · Cited by 431
Foreground-Aware Image Inpainting
Xiong, W., Yu, J., Lin, Z., Yang, J., Lu, X., Barnes, C., & Luo, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00599 · Cited by 424
Self-citationTemporal Generative Adversarial Nets with Singular Value Clipping
Saito, M., Matsumoto, E., & Saito, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.308 · Cited by 422
SynSin: End-to-End View Synthesis From a Single Image
Wiles, O., Gkioxari, G., Szeliski, R., & Johnson, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00749 · Cited by 418
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Zhou, H., Liu, Y., Liu, Z., Luo, P., & Wang, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019299 · Cited by 416
Geometric GANPreprint
Lim, J. H., & Ye, J. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.02894 · Cited by 403
GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisPreprint
Schwarz, K., Liao, Y., Niemeyer, M., & Geiger, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02442 · Cited by 400
Progressive Pose Attention Transfer for Person Image Generation
Zhu, Z., Huang, T., Shi, B., Yu, M., Wang, B., & Bai, X. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00245 · Cited by 388
Improved Precision and Recall Metric for Assessing Generative Models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., & Aila, T. (2019) · arXiv · doi:10.48550/arxiv.1904.06991 · Cited by 379
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired DataPreprint
Almahairi, A., Rajeswar, S., Sordoni, A., Bachman, P., & Courville, A. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10151 · Cited by 374
Real-time expression transfer for facial reenactment
Thies, J., Zollhöfer, M., Nießner, M., Valgaerts, L., Stamminger, M., & Theobalt, C. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818056 · Cited by 369
Stabilizing Training of Generative Adversarial Networks through RegularizationPreprint
Roth, K., Lucchi, A., Nowozin, S., & Hofmann, T. (2017) · arXiv (Cornell University) · doi:10.3929/ethz-b-000223162 · Cited by 362
Assessing Generative Models via Precision and RecallPreprint
Sajjadi, M. S. M., Bachem, O., Lučić, M., Bousquet, O., & Gelly, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00035 · Cited by 360
Animating Arbitrary Objects via Deep Motion Transfer
Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., & Sebe, N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00248 · Cited by 358
What Makes Fake Images Detectable? Understanding Properties that Generalize
Chai, L., Bau, D., Lim, S.-N., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_7 · Cited by 357
ARCH: Animatable Reconstruction of Clothed Humans
Huang, Z., Xu, Y., Lassner, C., Li, H., & Tung, T. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00316 · Cited by 353
Dual Motion GAN for Future-Flow Embedded Video Prediction
Liang, X., Lee, L., Dai, W., & Xing, E. P. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.194 · Cited by 349
Recycle-GAN: Unsupervised Video Retargeting
Bansal, A., Ma, S., Ramanan, D., & Sheikh, Y. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_8 · Cited by 346
Ladder Variational AutoencodersPreprint
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., & Winther, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.02282 · Cited by 346
SC-FEGAN: Face Editing Generative Adversarial Network With User’s Sketch and Color
Jo, Y., & Park, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00183 · Cited by 346
Seeing What a GAN Cannot GeneratePreprint
(2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 346
The Pose Knows: Video Forecasting by Generating Pose Futures
Walker, J., Marino, K., Gupta, A., & Hebert, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.361 · Cited by 341
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00768 · Cited by 340
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional NetworksPreprint
Xue, T., Wu, J., Bouman, K. L., & Freeman, W. T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.02586 · Cited by 330
Synthesizing Images of Humans in Unseen Poses
Balakrishnan, G., Zhao, A. T., Dalca, A. V., Durand, F., & Guttag, J. V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00870 · Cited by 324
AutoGAN: Neural Architecture Search for Generative Adversarial Networks
Gong, X., Chang, S., Jiang, Y., & Wang, Z. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00332 · Cited by 323
Wide Activation for Efficient and Accurate Image Super-ResolutionPreprint
Yu, J., Fan, Y., Yang, J., Xu, N., Wang, Z., Wang, X., & Huang, T. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08718 · Cited by 323
Self-citationMulti-agent Diverse Generative Adversarial Networks
Ghosh, A., Kulharia, V., Namboodiri, V. P., Torr, P. H. S., & Dokania, P. K. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00888 · Cited by 322
Unsupervised Learning of Disentangled Representations from Video
Denton, E., & Birodkar, V. (2017) · neural information processing systems · Cited by 321
PixelSNAIL: An Improved Autoregressive Generative ModelPreprint
Chen, X., Mishra, N., Rohaninejad, M., & Abbeel, P. (2017) · International Conference on Machine Learning · doi:10.48550/arxiv.1712.09763 · Cited by 319
Realistic Speech-Driven Facial Animation with GANs
Vougioukas, K., Petridis, S., & Pantić, M. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01251-8 · Cited by 317
GANalyze: Toward Visual Definitions of Cognitive Image Properties
Goetschalckx, L., Andonian, A., Oliva, A., & Isola, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00584 · Cited by 294
cGANs with Projection Discriminator
Miyato, T., & Koyama, M. (2018) · Cited by 293
Lip Movements Generation at a Glance
Chen, L., Li, Z., Maddox, R. K., Duan, Z., & Xu, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_32 · Cited by 281
Image Inpainting via Generative Multi-column Convolutional Neural NetworksPreprint
Wang, Y., Tao, X., Qi, X., Shen, X., & Jia, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08771 · Cited by 277
Unsupervised Attention-guided Image to Image TranslationPreprint
Mejjati, Y. A., Richardt, C., Tompkin, J., Cosker, D. P., & Kim, K. I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.02311 · Cited by 274
Text-based Editing of Talking-head Video
Fried, O., Tewari, A., Zollhöfer, M., Finkelstein, A., Shechtman, E., Goldman, D. B., Genova, K., Jin, Z., Theobalt, C., & Agrawala, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323028 · Cited by 270
Few-shot Video-to-Video SynthesisPreprint
Wang, T.-C., Liu, M.-Y., Tao, A., Liu, G., Kautz, J., & Catanzaro, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12713 · Cited by 269
Self-citationLiu, W., Piao, Z., Min, J., Luo, W., Ma, L., & Gao, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00600 · Cited by 266
Cross-Domain Correspondence Learning for Exemplar-Based Image Translation
Zhang, P., Zhang, B., Chen, D., Yuan, L., & Wen, F. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00519 · Cited by 260
One-Sided Unsupervised Domain MappingPreprint
Benaim, S., & Wolf, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00826 · Cited by 256
Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyPreprint
Wang, Z., She, Q., & Ward, T. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01529 · Cited by 254
Video Pixel NetworksPreprint
Kalchbrenner, N., van den Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.00527 · Cited by 241
Deep Generative Adversarial Compression Artifact Removal
Galteri, L., Seidenari, L., Bertini, M., & Bimbo, A. D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.517 · Cited by 222
Shen, Z., Lai, W., Xu, T., Kautz, J., & Yang, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00862 · Cited by 218
Talking Face Generation by Conditional Recurrent Adversarial Network
Song, Y., Zhu, J., Li, D., Wang, A. Y.-D., & Qi, H. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/129 · Cited by 214
Deep Image Spatial Transformation for Person Image Generation
Ren, Y., Yu, X., Chen, J., Li, T. H., & Li, G. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00771 · Cited by 214
Neverova, N., Güler, R. A., & Kokkinos, I. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_8 · Cited by 210
ReenactGAN: Learning to Reenact Faces via Boundary Transfer
Wu, W., Zhang, Y., Li, C., Qian, C., & Loy, C. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_37 · Cited by 207
Dense Intrinsic Appearance Flow for Human Pose Transfer
Li, Y., Huang, C., & Loy, C. C. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00381 · Cited by 203
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00255 · Cited by 198
Unpaired Image Super-Resolution Using Pseudo-Supervision
Maeda, S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00037 · Cited by 195
Multi-View Image Generation from a Single-View
Zhao, B., Wu, X. Y., Cheng, Z.-Q., Liu, H., Jie, Z., & Feng, J. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240536 · Cited by 193
Attention-GAN for Object Transfiguration in Wild Images
Chen, X., Xu, C., Yang, X., & Tao, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_11 · Cited by 193
You Said That?: Synthesising Talking Faces from Audio
Jamaludin, A., Chung, J. S., & Zisserman, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01150-y · Cited by 189
GANimation: One-Shot Anatomically Consistent Facial Animation
Pumarola, A., Agudo, A., Martı́nez, A. M., Sanfeliu, A., & Moreno-Noguer, F. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01210-3 · Cited by 187
Averbuch‐Elor, H., Cohen‐Or, D., Kopf, J., & Cohen, M. F. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130818 · Cited by 187
Neural Rerendering in the Wild
Meshry, M., Goldman, D. B., Khamis, S., Hoppe, H., Pandey, R., Snavely, N., & Martin-Brualla, R. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00704 · Cited by 185
Diversity-Sensitive Conditional Generative Adversarial NetworksPreprint
Yang, D., Hong, S., Jang, Y., Zhao, T., & Lee, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09024 · Cited by 182
InstaGAN: Instance-aware Image-to-Image TranslationPreprint
Mo, S., Cho, M., & Shin, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.10889 · Cited by 170
Research works printed but not linked: 8
- goodfellow, 2016, Deep Learning
- lei ba, 2016, Layer normalization, arXiv 1607 06450
- 10.1109/CVPR.2018.00762 · DOI
- kingma, 2018, Glow: Generative flow with invertible $1\times1$ convolutions, Proc NeurIPS, 10215
- tieleman, 2012, Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude, Proc Neural Netw Mach Learn (COURSERA)
- 10.1007/978-3-030-01261-8_1 · DOI · Paper page
- 10.1109/ICCV.2019.00460 · DOI
- bi?kowski, 2018, Demystifying MMD GANs, Proc ICLR
Cited by
Sorted by citations
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
Niemeyer, M., & Geiger, A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01129 · Cited by 777
Deep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464
Deep learning for deepfakes creation and detection: A survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103525 · Cited by 464
“nt common underlying mechanism for deepfake creation is deep learning models such as autoencoders and generative adversarial networks, which have been applied widely in the computer vision domain [2]–[4]. These models are used to examine facial expressions and movements of a person and synthesize facial images of anot…”
BackgroundOne-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
Wang, T.-C., Mallya, A., & Liu, M.-Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00991 · Cited by 496
Self-citationPaint by Example: Exemplar-based Image Editing with Diffusion Models
Yang, B., Gu, S., Zhang, B., Zhang, T., Chen, X., Sun, X., Chen, D., & Wen, F. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01763 · Cited by 459
Metadata
- Authors
- Liu, M.-Y., Huang, X., Yu, J., Wang, T.-C., & Mallya, A.
- Affiliations
- Nvidia (United States) (US)
- Google (United States) (US)
- Venue
- Proceedings of the IEEE
- Published
- 2021-05
- Volume
- 109
- Issue
- 5
- Pages
- 839-862
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W3126434301
- Cited by
- 236
- References
- 287
- Citations by year
- 2026: 16 · 2025: 51 · 2024: 63 · 2023: 39 · 2022: 37 · 2021: 27
- FWCI
- 14.93
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings of the IEEE (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- generative adversarial networks, image generation, video generation, GAN training stability, image-to-image translation, image processing, neural rendering, realistic image synthesis, conditional generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
