{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T10:59:32Z","timestamp":1785754772554,"version":"3.56.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>Portraiture as an art form has evolved from realistic depiction into a plethora of creative styles. While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images. Such StyleGAN-based methods have focused on finding the best latent inversion mapping for reconstructing input images; however, our key insight is that this does not lead to good generalization to different portrait styles. Hence we propose AgileGAN, a framework that can generate high quality stylistic portraits via inversion-consistent transfer learning. We introduce a novel hierarchical variational autoencoder to ensure the inverse mapped distribution conforms to the original latent Gaussian distribution, while augmenting the original space to a multi-resolution latent space so as to better encode different levels of detail. To better capture attribute-dependent stylization of facial features, we also present an attribute-aware generator and adopt an early stopping strategy to avoid overfitting small training datasets. Our approach provides greater agility in creating high quality and high resolution (1024\u00d71024) portrait stylization models, requiring only a limited number of style exemplars (~100) and short training time (~1 hour). We collected several style datasets for evaluation including 3D cartoons, comics, oil paintings and celebrities. We show that we can achieve superior portrait stylization quality to previous state-of-the-art methods, with comparisons done qualitatively, quantitatively and through a perceptual user study. We also demonstrate two applications of our method, image editing and motion retargeting.<\/jats:p>","DOI":"10.1145\/3450626.3459771","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:27Z","timestamp":1626739467000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":82,"title":["AgileGAN"],"prefix":"10.1145","volume":"40","author":[{"given":"Guoxian","family":"Song","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linjie","family":"Luo","sequence":"additional","affiliation":[{"name":"ByteDance Inc"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"ByteDance Inc"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan-Chun","family":"Ma","sequence":"additional","affiliation":[{"name":"ByteDance Inc"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunpong","family":"Lai","sequence":"additional","affiliation":[{"name":"ByteDance Inc"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanxia","family":"Zheng","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tat-Jen","family":"Cham","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Rameen Abdal Yipeng Qin and Peter Wonka. 2019a. Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?. In ICCV.  Rameen Abdal Yipeng Qin and Peter Wonka. 2019a. Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?. In ICCV.","DOI":"10.1109\/ICCV.2019.00453"},{"key":"e_1_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Rameen Abdal Yipeng Qin and Peter Wonka. 2019b. Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?. In ICCV.  Rameen Abdal Yipeng Qin and Peter Wonka. 2019b. Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?. In ICCV.","DOI":"10.1109\/ICCV.2019.00453"},{"key":"e_1_2_2_3_1","doi-asserted-by":"crossref","unstructured":"David Bau Hendrik Strobelt William Peebles Jonas Wulff Bolei Zhou JunYan Zhu and Antonio Torralba. 2019a. Semantic Photo Manipulation with a Generative Image Prior. In ACM Transactions on Graphics.  David Bau Hendrik Strobelt William Peebles Jonas Wulff Bolei Zhou JunYan Zhu and Antonio Torralba. 2019a. Semantic Photo Manipulation with a Generative Image Prior. In ACM Transactions on Graphics.","DOI":"10.1145\/3306346.3323023"},{"key":"e_1_2_2_4_1","doi-asserted-by":"crossref","unstructured":"David Bau Jun-Yan Zhu Jonas Wulff William Peebles Hendrik Strobelt Bolei Zhou and Antonio Torralba. 2019b. Seeing What a GAN Cannot Generate. In ICCV.  David Bau Jun-Yan Zhu Jonas Wulff William Peebles Hendrik Strobelt Bolei Zhou and Antonio Torralba. 2019b. Seeing What a GAN Cannot Generate. In ICCV.","DOI":"10.1109\/ICCV.2019.00460"},{"key":"e_1_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Jiankang Deng Jia Guo Xue Niannan and Stefanos Zafeiriou. 2019. ArcFace: Additive Angular Margin Loss for Deep Face Recognition. In CVPR.  Jiankang Deng Jia Guo Xue Niannan and Stefanos Zafeiriou. 2019. ArcFace: Additive Angular Margin Loss for Deep Face Recognition. In CVPR.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2014.2359646"},{"key":"e_1_2_2_7_1","doi-asserted-by":"crossref","unstructured":"L. A. Gatys A. S. Ecker and M. Bethge. 2016. Image Style Transfer Using Convolutional Neural Networks. In CVPR.  L. A. Gatys A. S. Ecker and M. Bethge. 2016. Image Style Transfer Using Convolutional Neural Networks. In CVPR.","DOI":"10.1109\/CVPR.2016.265"},{"key":"e_1_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Baris Gecer Alexander Lattas Stylianos Ploumpis Jiankang Deng Athanasios Papaioannou Stylianos Moschoglou and Stefanos Zafeiriou. 2020. Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks. In ECCV.  Baris Gecer Alexander Lattas Stylianos Ploumpis Jiankang Deng Athanasios Papaioannou Stylianos Moschoglou and Stefanos Zafeiriou. 2020. Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks. In ECCV.","DOI":"10.1007\/978-3-030-58526-6_25"},{"key":"e_1_2_2_9_1","volume-title":"Proc. NeurIPS.","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014 . Generative Adversarial Nets . In Proc. NeurIPS. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Proc. NeurIPS."},{"key":"e_1_2_2_10_1","unstructured":"Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin and Aaron Courville. 2017. Improved Training of Wasserstein GANs. In NeurIPS.  Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin and Aaron Courville. 2017. Improved Training of Wasserstein GANs. In NeurIPS."},{"key":"e_1_2_2_11_1","article-title":"Pyramid-Based Texture Analysis\/Synthesis. In ACM","author":"Heeger David J.","year":"1995","unstructured":"David J. Heeger and James R. Bergen . 1995 . Pyramid-Based Texture Analysis\/Synthesis. In ACM Trans. Graph. David J. Heeger and James R. Bergen. 1995. Pyramid-Based Texture Analysis\/Synthesis. In ACM Trans. Graph.","journal-title":"Trans. Graph."},{"key":"e_1_2_2_12_1","unstructured":"Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems I. Guyon U. V. Luxburg S. Bengio H. Wallach R. Fergus S. Vishwanathan and R. Garnett (Eds.).  Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems I. Guyon U. V. Luxburg S. Bengio H. Wallach R. Fergus S. Vishwanathan and R. Garnett (Eds.)."},{"key":"e_1_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Xun Huang and Serge Belongie. 2017. Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. In ICCV.  Xun Huang and Serge Belongie. 2017. Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. In ICCV.","DOI":"10.1109\/ICCV.2017.167"},{"key":"e_1_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Xun Huang Ming-Yu Liu Serge Belongie and Jan Kautz. 2018. Multimodal Unsupervised Image-to-image Translation. In ECCV.  Xun Huang Ming-Yu Liu Serge Belongie and Jan Kautz. 2018. Multimodal Unsupervised Image-to-image Translation. In ECCV.","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"e_1_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Phillip Isola Jun-Yan Zhu Tinghui Zhou and Alexei A Efros. 2017. Image-to-Image Translation with Conditional Adversarial Networks. In CVPR.  Phillip Isola Jun-Yan Zhu Tinghui Zhou and Alexei A Efros. 2017. Image-to-Image Translation with Conditional Adversarial Networks. In CVPR.","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Justin Johnson Alexandre Alahi and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In ECCV.  Justin Johnson Alexandre Alahi and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In ECCV.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_2_2_17_1","volume-title":"Proc. NeurIPS.","author":"Karacan Levent","year":"2016","unstructured":"Levent Karacan , Zeynep Akata , Aykut Erdem , and Erkut Erdem . 2016 . Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts . In Proc. NeurIPS. Levent Karacan, Zeynep Akata, Aykut Erdem, and Erkut Erdem. 2016. Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts. In Proc. NeurIPS."},{"key":"e_1_2_2_18_1","volume-title":"Proc. NeurIPS.","author":"Karras Tero","year":"2020","unstructured":"Tero Karras , Miika Aittala , Janne Hellsten , Samuli Laine , Jaakko Lehtinen , and Timo Aila . 2020 a. Training Generative Adversarial Networks with Limited Data . In Proc. NeurIPS. Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2020a. Training Generative Adversarial Networks with Limited Data. In Proc. NeurIPS."},{"key":"e_1_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Tero Karras Samuli Laine and Timo Aila. 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. In CVPR.  Tero Karras Samuli Laine and Timo Aila. 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. In CVPR.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Tero Karras Samuli Laine Miika Aittala Janne Hellsten Jaakko Lehtinen and Timo Aila. 2020b. Analyzing and Improving the Image Quality of StyleGAN. In CVPR.  Tero Karras Samuli Laine Miika Aittala Janne Hellsten Jaakko Lehtinen and Timo Aila. 2020b. Analyzing and Improving the Image Quality of StyleGAN. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_2_2_21_1","volume-title":"U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation. In International Conference on Learning Representations.","author":"Kim Junho","year":"2020","unstructured":"Junho Kim , Minjae Kim , Hyeonwoo Kang , and Kwang Hee Lee . 2020 . U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation. In International Conference on Learning Representations. Junho Kim, Minjae Kim, Hyeonwoo Kang, and Kwang Hee Lee. 2020. U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation. In International Conference on Learning Representations."},{"key":"e_1_2_2_22_1","unstructured":"Diederik P. Kingma and M. Welling. 2014. Auto-Encoding Variational Bayes. (2014).  Diederik P. Kingma and M. Welling. 2014. Auto-Encoding Variational Bayes. (2014)."},{"key":"e_1_2_2_23_1","unstructured":"Cheng-Han Lee Ziwei Liu Lingyun Wu and Ping Luo. 2020. MaskGAN: Towards Diverse and Interactive Facial Image Manipulation. In CVPR.  Cheng-Han Lee Ziwei Liu Lingyun Wu and Ping Luo. 2020. MaskGAN: Towards Diverse and Interactive Facial Image Manipulation. In CVPR."},{"key":"e_1_2_2_24_1","unstructured":"Chuan Li and Michael Wand. 2016. Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis. In CVPR.  Chuan Li and Michael Wand. 2016. Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis. In CVPR."},{"key":"e_1_2_2_25_1","unstructured":"Jerry Li. 2018. Twin-GAN - Unpaired Cross-Domain Image Translation with Weight-Sharing GANs.  Jerry Li. 2018. Twin-GAN - Unpaired Cross-Domain Image Translation with Weight-Sharing GANs."},{"key":"e_1_2_2_26_1","doi-asserted-by":"crossref","unstructured":"T. Lin P. Doll\u00e1r R. Girshick K. He B. Hariharan and S. Belongie. 2017. Feature Pyramid Networks for Object Detection. In CVPR.  T. Lin P. Doll\u00e1r R. Girshick K. He B. Hariharan and S. Belongie. 2017. Feature Pyramid Networks for Object Detection. In CVPR.","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_2_2_27_1","volume-title":"Proceedings of the Eighth International Conference on Learning Representations (ICLR","author":"Liu Liyuan","year":"2020","unstructured":"Liyuan Liu , Haoming Jiang , Pengcheng He , Weizhu Chen , Xiaodong Liu , Jianfeng Gao , and Jiawei Han . 2020 . On the Variance of the Adaptive Learning Rate and Beyond . In Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020). Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 2020. On the Variance of the Adaptive Learning Rate and Beyond. In Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020)."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294838"},{"key":"e_1_2_2_29_1","unstructured":"Ming-Yu Liu Xun Huang Arun Mallya Tero Karras Timo Aila Jaakko Lehtinen and Jan Kautz. 2019. Few-shot Unsueprvised Image-to-Image Translation. In CVPR.  Ming-Yu Liu Xun Huang Arun Mallya Tero Karras Timo Aila Jaakko Lehtinen and Jan Kautz. 2019. Few-shot Unsueprvised Image-to-Image Translation. In CVPR."},{"key":"e_1_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Ziwei Liu Ping Luo Xiaogang Wang and Xiaoou Tang. 2015. Deep Learning Face Attributes in the Wild. In ICCV.  Ziwei Liu Ping Luo Xiaogang Wang and Xiaoou Tang. 2015. Deep Learning Face Attributes in the Wild. In ICCV.","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_2_2_31_1","doi-asserted-by":"crossref","unstructured":"X. Mao Q. Li H. Xie R. Y. K. Lau Z. Wang and S. P. Smolley. 2017. Least Squares Generative Adversarial Networks. In ICCV.  X. Mao Q. Li H. Xie R. Y. K. Lau Z. Wang and S. P. Smolley. 2017. Least Squares Generative Adversarial Networks. In ICCV.","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_2_2_32_1","volume-title":"PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. In CVPR.","author":"Menon Sachit","year":"2020","unstructured":"Sachit Menon , Alexandru Damian , Shijia Hu , Nikhil Ravi , and Cynthia Rudin . 2020 . PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. In CVPR. Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin. 2020. PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. In CVPR."},{"key":"e_1_2_2_33_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Mescheder Lars","year":"2018","unstructured":"Lars Mescheder , Sebastian Nowozin , and Andreas Geiger . 2018 . Which Training Methods for GANs do actually Converge? . In International Conference on Machine Learning (ICML). Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. 2018. Which Training Methods for GANs do actually Converge?. In International Conference on Machine Learning (ICML)."},{"key":"e_1_2_2_34_1","volume-title":"Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains. In NeurIPS Workshop.","author":"Justin N.","unstructured":"Justin N. M. Pinkney and Doron Adler. 2020 . Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains. In NeurIPS Workshop. Justin N. M. Pinkney and Doron Adler. 2020. Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains. In NeurIPS Workshop."},{"key":"e_1_2_2_35_1","unstructured":"pinterest 2021. pinterest. https:\/\/www.pinterest.com\/.  pinterest 2021. pinterest. https:\/\/www.pinterest.com\/."},{"key":"e_1_2_2_36_1","volume-title":"Stochastic Back-propagation and Approximate Inference in Deep Generative Models. In International Conference on International Conference on Machine Learning.","author":"Rezende Danilo Jimenez","year":"2014","unstructured":"Danilo Jimenez Rezende , Shakir Mohamed , and Daan Wierstra . 2014 . Stochastic Back-propagation and Approximate Inference in Deep Generative Models. In International Conference on International Conference on Machine Learning. Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic Back-propagation and Approximate Inference in Deep Generative Models. In International Conference on International Conference on Machine Learning."},{"key":"e_1_2_2_37_1","volume-title":"Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation. arXiv preprint arXiv:2008.00951","author":"Richardson Elad","year":"2020","unstructured":"Elad Richardson , Yuval Alaluf , Or Patashnik , Yotam Nitzan , Yaniv Azar , Stav Shapiro , and Daniel Cohen-Or . 2020. Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation. arXiv preprint arXiv:2008.00951 ( 2020 ). Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. 2020. Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation. arXiv preprint arXiv:2008.00951 (2020)."},{"key":"e_1_2_2_38_1","volume-title":"Artistic Style Transfer for Videos. In German Conference on Pattern Recognition.","author":"Ruder Manuel","year":"2016","unstructured":"Manuel Ruder , Alexey Dosovitskiy , and Thomas Brox . 2016 . Artistic Style Transfer for Videos. In German Conference on Pattern Recognition. Manuel Ruder, Alexey Dosovitskiy, and Thomas Brox. 2016. Artistic Style Transfer for Videos. In German Conference on Pattern Recognition."},{"key":"e_1_2_2_39_1","volume-title":"Scribbler: Controlling Deep Image Synthesis with Sketch and Color. In CVPR.","author":"Sangkloy P.","year":"2017","unstructured":"P. Sangkloy , J. Lu , C. Fang , F. Yu , and J. Hays . 2017 . Scribbler: Controlling Deep Image Synthesis with Sketch and Color. In CVPR. P. Sangkloy, J. Lu, C. Fang, F. Yu, and J. Hays. 2017. Scribbler: Controlling Deep Image Synthesis with Sketch and Color. In CVPR."},{"key":"e_1_2_2_40_1","doi-asserted-by":"crossref","unstructured":"T. R. Shaham T. Dekel and T. Michaeli. 2019. SinGAN: Learning a Generative Model From a Single Natural Image. In ICCV.  T. R. Shaham T. Dekel and T. Michaeli. 2019. SinGAN: Learning a Generative Model From a Single Natural Image. In ICCV.","DOI":"10.1109\/ICCV.2019.00467"},{"key":"e_1_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Yujun Shen and Bolei Zhou. 2020. Closed-Form Factorization of Latent Semantics in GANs. In ECCV.  Yujun Shen and Bolei Zhou. 2020. Closed-Form Factorization of Latent Semantics in GANs. In ECCV.","DOI":"10.1109\/CVPR46437.2021.00158"},{"key":"e_1_2_2_42_1","doi-asserted-by":"crossref","unstructured":"A. Shocher N. Cohen and M. Irani. 2018. Zero-Shot Super-Resolution Using Deep Internal Learning. In CVPR.  A. Shocher N. Cohen and M. Irani. 2018. Zero-Shot Super-Resolution Using Deep Internal Learning. In CVPR.","DOI":"10.1109\/CVPR.2018.00329"},{"key":"e_1_2_2_43_1","unstructured":"Aliaksandr Siarohin St\u00e9phane Lathuili\u00e8re Sergey Tulyakov Elisa Ricci and Nicu Sebe. 2019. First Order Motion Model for Image Animation. In NeurIPS.  Aliaksandr Siarohin St\u00e9phane Lathuili\u00e8re Sergey Tulyakov Elisa Ricci and Nicu Sebe. 2019. First Order Motion Model for Image Animation. In NeurIPS."},{"key":"e_1_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Guoxian Song Jianmin Zheng Jianfei Cai and Tat-Jen Cham. 2020. Recovering facial reflectance and geometry from multi-view images. In Image and Vision Computing.  Guoxian Song Jianmin Zheng Jianfei Cai and Tat-Jen Cham. 2020. Recovering facial reflectance and geometry from multi-view images. In Image and Vision Computing.","DOI":"10.1016\/j.imavis.2020.103897"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417803"},{"key":"e_1_2_2_46_1","unstructured":"turbosquid 2021. turbosquid. https:\/\/www.turbosquid.com\/Search\/3D-Models\/.  turbosquid 2021. turbosquid. https:\/\/www.turbosquid.com\/Search\/3D-Models\/."},{"key":"e_1_2_2_47_1","unstructured":"Ting-Chun Wang Ming-Yu Liu Andrew Tao Guilin Liu Jan Kautz and Bryan Catanzaro. 2019. Few-shot Video-to-Video Synthesis. In NeurIPS.  Ting-Chun Wang Ming-Yu Liu Andrew Tao Guilin Liu Jan Kautz and Bryan Catanzaro. 2019. Few-shot Video-to-Video Synthesis. In NeurIPS."},{"key":"e_1_2_2_48_1","unstructured":"Ting-Chun Wang Ming-Yu Liu Jun-Yan Zhu Andrew Tao Jan Kautz and Bryan Catanzaro. 2018. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. In CVPR.  Ting-Chun Wang Ming-Yu Liu Jun-Yan Zhu Andrew Tao Jan Kautz and Bryan Catanzaro. 2018. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. In CVPR."},{"key":"e_1_2_2_49_1","volume-title":"Conference on Neural Information Processing Systems.","author":"Wulff Jonas","year":"2020","unstructured":"Jonas Wulff and Antonio Torralba . 2020 . Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space . In Conference on Neural Information Processing Systems. Jonas Wulff and Antonio Torralba. 2020. Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space. In Conference on Neural Information Processing Systems."},{"key":"e_1_2_2_50_1","doi-asserted-by":"crossref","unstructured":"L. Yuan C. Ruan H. Hu and D. Chen. 2019. Image Inpainting Based on Patch-GANs. In IEEE Access.  L. Yuan C. Ruan H. Hu and D. Chen. 2019. Image Inpainting Based on Patch-GANs. In IEEE Access.","DOI":"10.1109\/ACCESS.2019.2909553"},{"key":"e_1_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Richard Zhang Phillip Isola Alexei A Efros Eli Shechtman and Oliver Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In CVPR.  Richard Zhang Phillip Isola Alexei A Efros Eli Shechtman and Oliver Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In CVPR.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"e_1_2_2_52_1","unstructured":"Jiapeng Zhu Yujun Shen Deli Zhao and Bolei Zhou. 2020. In-domain GAN Inversion for Real Image Editing. In ECCV.  Jiapeng Zhu Yujun Shen Deli Zhao and Bolei Zhou. 2020. In-domain GAN Inversion for Real Image Editing. In ECCV."},{"key":"e_1_2_2_53_1","volume-title":"Efros","author":"Zhu Jun-Yan","year":"2016","unstructured":"Jun-Yan Zhu , Philipp Kr\u00e4henb\u00fchl , Eli Shechtman , and Alexei A . Efros . 2016 . Generative Visual Manipulation on the Natural Image Manifold. In ECCV. Jun-Yan Zhu, Philipp Kr\u00e4henb\u00fchl, Eli Shechtman, and Alexei A. Efros. 2016. Generative Visual Manipulation on the Natural Image Manifold. In ECCV."},{"key":"e_1_2_2_54_1","unstructured":"Jun-Yan Zhu Taesung Park Phillip Isola and Alexei A Efros. 2017. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss. In ICCV.  Jun-Yan Zhu Taesung Park Phillip Isola and Alexei A Efros. 2017. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss. In ICCV."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450626.3459771","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450626.3459771","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:16Z","timestamp":1750191436000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450626.3459771"}},"subtitle":["stylizing portraits by inversion-consistent transfer learning"],"short-title":[],"issued":{"date-parts":[[2021,7,19]]},"references-count":54,"aliases":["10.1145\/3476576.3476684"],"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,8,31]]}},"alternative-id":["10.1145\/3450626.3459771"],"URL":"https:\/\/doi.org\/10.1145\/3450626.3459771","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,19]]},"assertion":[{"value":"2021-07-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}