{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T02:32:25Z","timestamp":1776738745714,"version":"3.51.2"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62201342, 62101325, and U19B2035"],"award-info":[{"award-number":["62201342, 62101325, and U19B2035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0102"],"award-info":[{"award-number":["2021SHZDZX0102"]}]},{"name":"CCF-Alibaba Innovative Research Fund For Young Scholars"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications. However, current full-head generation methods require a large number of three-dimensional (3D) scans or multi-view images to train the model, resulting in expensive data acquisition costs. To address this issue, we propose Head3D, a method to generate full 3D heads with limited multi-view images. Specifically, our approach first extracts facial priors represented by tri-planes learned in EG3D, a 3D-aware generative model, and then proposes feature distillation to deliver the 3D frontal faces within complete heads without compromising head integrity. To mitigate the domain gap between the face and head models, we present a dual-discriminator to guide the frontal and back head generation. Our model achieves cost-efficient and diverse complete head generation with photo-realistic renderings and high-quality geometry representations. Extensive experiments demonstrate the effectiveness of our proposed Head3D, both qualitatively and quantitatively.<\/jats:p>","DOI":"10.1145\/3635717","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T12:42:06Z","timestamp":1706186526000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3174-8973","authenticated-orcid":false,"given":"Yuhao","family":"Cheng","sequence":"first","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3209-8965","authenticated-orcid":false,"given":"Yichao","family":"Yan","sequence":"additional","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6448-4217","authenticated-orcid":false,"given":"Wenhan","family":"Zhu","sequence":"additional","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1447-6806","authenticated-orcid":false,"given":"Ye","family":"Pan","sequence":"additional","affiliation":[{"name":"John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8711-7433","authenticated-orcid":false,"given":"Bowen","family":"Pan","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-3322","authenticated-orcid":false,"given":"Xiaokang","family":"Yang","sequence":"additional","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"20950","volume-title":"CVPR","author":"An Sizhe","year":"2023","unstructured":"Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Y. Ogras, and Linjie Luo. 2023. PanoHead: Geometry-aware 3D full-head synthesis in 360deg. In CVPR. 20950\u201320959."},{"key":"e_1_3_1_3_2","article-title":"Demystifying MMD GANs","author":"Bi\u0144kowski Miko\u0142aj","year":"2018","unstructured":"Miko\u0142aj Bi\u0144kowski, Danica J. Sutherland, Michael Arbel, and Arthur Gretton. 2018. Demystifying MMD GANs. arXiv preprint arXiv:1801.01401 (2018).","journal-title":"arXiv preprint arXiv:1801.01401"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Volker Blanz and Thomas Vetter. 1999. A morphable model for the synthesis of 3D faces. In SIGGRAPH187\u2013194.","DOI":"10.1145\/311535.311556"},{"key":"e_1_3_1_5_2","first-page":"48","volume-title":"CVPR","author":"Booth James","year":"2017","unstructured":"James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, and Stefanos Zafeiriou. 2017. 3D face morphable models \u201cin-the-wild\u201d. In CVPR. 48\u201357."},{"key":"e_1_3_1_6_2","first-page":"5543","volume-title":"CVPR","author":"Booth James","year":"2016","unstructured":"James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David Dunaway. 2016. A 3D morphable model learnt from 10,000 faces. In CVPR. 5543\u20135552."},{"key":"e_1_3_1_7_2","article-title":"Multi-NeuS: 3D head portraits from single image with neural implicit functions","author":"Burkov Egor","year":"2022","unstructured":"Egor Burkov, Ruslan Rakhimov, Aleksandr Safin, Evgeny Burnaev, and Victor Lempitsky. 2022. Multi-NeuS: 3D head portraits from single image with neural implicit functions. arXiv preprint arXiv:2209.04436 (2022).","journal-title":"arXiv preprint arXiv:2209.04436"},{"key":"e_1_3_1_8_2","first-page":"413","article-title":"FaceWarehouse: A 3D facial expression database for visual computing","author":"Cao Chen","year":"2013","unstructured":"Chen Cao, Yanlin Weng, Shun Zhou, Yiying Tong, and Kun Zhou. 2013. FaceWarehouse: A 3D facial expression database for visual computing. IEEE TVCG (2013), 413\u2013425.","journal-title":"IEEE TVCG"},{"key":"e_1_3_1_9_2","first-page":"16123","volume-title":"CVPR","author":"Chan Eric R.","year":"2022","unstructured":"Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J. Guibas, Jonathan Tremblay, Sameh Khamis, et\u00a0al. 2022. Efficient geometry-aware 3D generative adversarial networks. In CVPR. 16123\u201316133."},{"key":"e_1_3_1_10_2","first-page":"5799","volume-title":"CVPR","author":"Chan Eric R.","year":"2021","unstructured":"Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. 2021. pi-GAN: Periodic implicit generative adversarial networks for 3D-aware image synthesis. In CVPR. 5799\u20135809."},{"key":"e_1_3_1_11_2","first-page":"333","volume-title":"ECCV","author":"Chen Anpei","year":"2022","unstructured":"Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. 2022. Tensorf: Tensorial radiance fields. In ECCV. Springer, 333\u2013350."},{"key":"e_1_3_1_12_2","volume-title":"AAAI","author":"Chen Hanting","year":"2020","unstructured":"Hanting Chen, Yunhe Wang, Han Shu, Changyuan Wen, Chunjing Xu, Boxin Shi, Chao Xu, and Chang Xu. 2020. Distilling portable generative adversarial networks for image translation. In AAAI."},{"key":"e_1_3_1_13_2","first-page":"4794","volume-title":"ICCV","author":"Cho Jang Hyun","year":"2019","unstructured":"Jang Hyun Cho and Bharath Hariharan. 2019. On the efficacy of knowledge distillation. In ICCV. 4794\u20134802."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.335"},{"key":"e_1_3_1_15_2","first-page":"4690","volume-title":"CVPR","author":"Deng Jiankang","year":"2019","unstructured":"Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. 2019. ArcFace: Additive angular margin loss for deep face recognition. In CVPR. 4690\u20134699."},{"key":"e_1_3_1_16_2","first-page":"0","volume-title":"CVPR Workshops","author":"Deng Yu","year":"2019","unstructured":"Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. 2019. Accurate 3D face reconstruction with weakly-supervised learning: From single image to image set. In CVPR Workshops. 0\u20130."},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450626.3459936","article-title":"Learning an animatable detailed 3D face model from in-the-wild images","author":"Feng Yao","year":"2021","unstructured":"Yao Feng, Haiwen Feng, Michael J. Black, and Timo Bolkart. 2021. Learning an animatable detailed 3D face model from in-the-wild images. TOG (2021), 1\u201313.","journal-title":"TOG"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530164"},{"key":"e_1_3_1_19_2","first-page":"4857","volume-title":"CVPR","author":"Genova Kyle","year":"2020","unstructured":"Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. 2020. Local deep implicit functions for 3D shape. In CVPR. 4857\u20134866."},{"key":"e_1_3_1_20_2","first-page":"75","volume-title":"FG","author":"Gerig Thomas","year":"2018","unstructured":"Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Luthi, Sandro Sch\u00f6nborn, and Thomas Vetter. 2018. Morphable face models\u2014an open framework. In FG. 75\u201382."},{"key":"e_1_3_1_21_2","article-title":"Learning neural parametric head models","author":"Giebenhain Simon","year":"2022","unstructured":"Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin R\u00fcnz, Lourdes Agapito, and Matthias Nie\u00dfner. 2022. Learning neural parametric head models. arXiv preprint arXiv:2212.02761 (2022).","journal-title":"arXiv preprint arXiv:2212.02761"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_3_1_23_2","first-page":"18653","volume-title":"CVPR","author":"Grassal Philip-William","year":"2022","unstructured":"Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nie\u00dfner, and Justus Thies. 2022. Neural head avatars from monocular RGB videos. In CVPR. 18653\u201318664."},{"key":"e_1_3_1_24_2","volume-title":"ICLR","author":"Gu Jiatao","year":"2022","unstructured":"Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. 2022. StyleNeRF: A style-based 3D aware generator for high-resolution image synthesis. In ICLR."},{"key":"e_1_3_1_25_2","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","author":"Heusel Martin","year":"2017","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. NeurIPS (2017).","journal-title":"NeurIPS"},{"key":"e_1_3_1_26_2","article-title":"Distilling the knowledge in a neural network","volume":"1503","author":"Hinton Geoffrey E.","year":"2015","unstructured":"Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015. Distilling the knowledge in a neural network. CoRR abs\/1503.02531 (2015).","journal-title":"CoRR"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3396237"},{"key":"e_1_3_1_28_2","first-page":"20374","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hong Yang","year":"2022","unstructured":"Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, and Juyong Zhang. 2022. HeadNeRF: A real-time NeRF-based parametric head model. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 20374\u201320384."},{"key":"e_1_3_1_29_1","volume-title":"AAAI","author":"Hou Liang","unstructured":"Liang Hou, Zehuan Yuan, Lei Huang, Huawei Shen, Xueqi Cheng, and Changhu Wang. [n. d.]. Slimmable generative adversarial networks. In AAAI."},{"key":"e_1_3_1_30_2","first-page":"12104","article-title":"Training generative adversarial networks with limited data","author":"Karras Tero","year":"2020","unstructured":"Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2020. Training generative adversarial networks with limited data. NeurIPS (2020), 12104\u201312114.","journal-title":"NeurIPS"},{"key":"e_1_3_1_31_2","first-page":"4401","volume-title":"CVPR","author":"Karras Tero","year":"2019","unstructured":"Tero Karras, Samuli Laine, and Timo Aila. 2019. A style-based generator architecture for generative adversarial networks. In CVPR. 4401\u20134410."},{"key":"e_1_3_1_32_2","volume-title":"CVPR","author":"Karras Tero","year":"2020","unstructured":"Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020. Analyzing and improving the image quality of StyleGAN. In CVPR."},{"key":"e_1_3_1_33_2","volume-title":"ICLR","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_1_34_2","first-page":"5284","volume-title":"CVPR","author":"Li Muyang","year":"2020","unstructured":"Muyang Li, Ji Lin, Yaoyao Ding, Zhijian Liu, Jun-Yan Zhu, and Song Han. 2020. GAN compression: Efficient architectures for interactive conditional GANs. In CVPR. 5284\u20135294."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3130800.3130813"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3432817"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3571746"},{"key":"e_1_3_1_38_2","first-page":"12156","volume-title":"CVPR","author":"Liu Yuchen","year":"2021","unstructured":"Yuchen Liu, Zhixin Shu, Yijun Li, Zhe Lin, Federico Perazzi, and Sun-Yuan Kung. 2021. Content-aware GAN compression. In CVPR. 12156\u201312166."},{"key":"e_1_3_1_39_2","first-page":"6351","volume-title":"ICCV","author":"Meng Quan","year":"2021","unstructured":"Quan Meng, Anpei Chen, Haimin Luo, Minye Wu, Hao Su, Lan Xu, Xuming He, and Jingyi Yu. 2021. GNeRF: GAN-based neural radiance field without posed camera. In ICCV. 6351\u20136361."},{"key":"e_1_3_1_40_2","first-page":"99","article-title":"NeRF: Representing scenes as neural radiance fields for view synthesis","author":"Mildenhall Ben","year":"2021","unstructured":"Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. 2021. NeRF: Representing scenes as neural radiance fields for view synthesis. Commun. ACM (2021), 99\u2013106.","journal-title":"Commun. ACM"},{"key":"e_1_3_1_41_2","first-page":"11453","volume-title":"CVPR","author":"Niemeyer Michael","year":"2021","unstructured":"Michael Niemeyer and Andreas Geiger. 2021. GIRAFFE: Representing scenes as compositional generative neural feature fields. In CVPR. 11453\u201311464."},{"key":"e_1_3_1_42_2","first-page":"13493","volume-title":"CVPR","author":"Or-El Roy","year":"2022","unstructured":"Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, and Ira Kemelmacher-Shlizerman. 2022. StyleSDF: High-resolution 3D-consistent image and geometry generation. In CVPR. 13493\u201313503."},{"key":"e_1_3_1_43_2","first-page":"3967","volume-title":"CVPR","author":"Park Wonpyo","year":"2019","unstructured":"Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho. 2019. Relational knowledge distillation. In CVPR. 3967\u20133976."},{"key":"e_1_3_1_44_2","first-page":"1327","volume-title":"CVPR","author":"Patel Ankur","year":"2009","unstructured":"Ankur Patel and William A. P. Smith. 2009. 3D morphable face models revisited. In CVPR. 1327\u20131334."},{"key":"e_1_3_1_45_2","first-page":"296","volume-title":"AVSS","author":"Paysan Pascal","year":"2009","unstructured":"Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. 2009. A 3D face model for pose and illumination invariant face recognition. In AVSS. 296\u2013301."},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2991150"},{"key":"e_1_3_1_47_2","first-page":"10934","volume-title":"CVPR","author":"Ploumpis Stylianos","year":"2019","unstructured":"Stylianos Ploumpis, Haoyang Wang, Nick Pears, William A. P. Smith, and Stefanos Zafeiriou. 2019. Combining 3D morphable models: A large scale face-and-head model. In CVPR. 10934\u201310943."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00557"},{"key":"e_1_3_1_49_2","article-title":"Fitnets: Hints for thin deep nets","author":"Romero Adriana","year":"2014","unstructured":"Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2014. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014).","journal-title":"arXiv preprint arXiv:1412.6550"},{"key":"e_1_3_1_50_2","first-page":"20154","article-title":"GRAF: Generative radiance fields for 3D-aware image synthesis","author":"Schwarz Katja","year":"2020","unstructured":"Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. 2020. GRAF: Generative radiance fields for 3D-aware image synthesis. NeurIPS (2020), 20154\u201320166.","journal-title":"NeurIPS"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3034267"},{"key":"e_1_3_1_52_2","volume-title":"ICLR","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In ICLR."},{"key":"e_1_3_1_53_2","first-page":"7462","article-title":"Implicit neural representations with periodic activation functions","author":"Sitzmann Vincent","year":"2020","unstructured":"Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. 2020. Implicit neural representations with periodic activation functions. NeurIPS (2020), 7462\u20137473.","journal-title":"NeurIPS"},{"key":"e_1_3_1_54_2","article-title":"EpiGRAF: Rethinking training of 3D GANs","volume":"2206","author":"Skorokhodov Ivan","year":"2022","unstructured":"Ivan Skorokhodov, Sergey Tulyakov, Yiqun Wang, and Peter Wonka. 2022. EpiGRAF: Rethinking training of 3D GANs. CoRR abs\/2206.10535 (2022).","journal-title":"CoRR"},{"key":"e_1_3_1_55_2","first-page":"7672","volume-title":"CVPR","author":"Sun Jingxiang","year":"2022","unstructured":"Jingxiang Sun, Xuan Wang, Yong Zhang, Xiaoyu Li, Qi Zhang, Yebin Liu, and Jue Wang. 2022. FENeRF: Face editing in neural radiance fields. In CVPR. 7672\u20137682."},{"key":"e_1_3_1_56_2","volume-title":"ICLR","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020. Contrastive representation distillation. In ICLR."},{"key":"e_1_3_1_57_2","first-page":"1126","volume-title":"CVPR","author":"Tran Luan","year":"2019","unstructured":"Luan Tran, Feng Liu, and Xiaoming Liu. 2019. Towards high-fidelity nonlinear 3D face morphable model. In CVPR. 1126\u20131135."},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530753"},{"key":"e_1_3_1_59_2","first-page":"54","volume-title":"ECCV","author":"Wang Haotao","year":"2020","unstructured":"Haotao Wang, Shupeng Gui, Haichuan Yang, Ji Liu, and Zhangyang Wang. 2020. GAN slimming: All-in-one GAN compression by a unified optimization framework. In ECCV. 54\u201373."},{"key":"e_1_3_1_60_2","article-title":"Rodin: A generative model for sculpting 3D digital avatars using diffusion","author":"Wang Tengfei","year":"2022","unstructured":"Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Baltrusaitis, Jingjing Shen, Dong Chen, Fang Wen, Qifeng Chen, et\u00a0al. 2022. Rodin: A generative model for sculpting 3D digital avatars using diffusion. arXiv preprint arXiv:2212.06135 (2022).","journal-title":"arXiv preprint arXiv:2212.06135"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3355397"},{"key":"e_1_3_1_62_2","first-page":"437","volume-title":"CVPR","author":"Wu Sijing","year":"2023","unstructured":"Sijing Wu, Yichao Yan, Yunhao Li, Yuhao Cheng, Wenhan Zhu, Ke Gao, Xiaobo Li, and Guangtao Zhai. 2023. GANHead: Towards generative animatable neural head avatars. In CVPR. 437\u2013447."},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01859"},{"key":"e_1_3_1_64_2","first-page":"2195","volume-title":"ICCV","author":"Xiang Jianfeng","year":"2023","unstructured":"Jianfeng Xiang, Jiaolong Yang, Yu Deng, and Xin Tong. 2023. GRAM-HD: 3D-consistent image generation at high resolution with generative radiance manifolds. In ICCV. 2195\u20132205."},{"key":"e_1_3_1_65_2","article-title":"Mind the gap in distilling StyleGANs","author":"Xu Guodong","year":"2022","unstructured":"Guodong Xu, Yuenan Hou, Ziwei Liu, and Chen Change Loy. 2022. Mind the gap in distilling StyleGANs. arXiv preprint arXiv:2208.08840 (2022).","journal-title":"arXiv preprint arXiv:2208.08840"},{"key":"e_1_3_1_66_2","volume-title":"ECCV","author":"Xu Guodong","year":"2020","unstructured":"Guodong Xu, Ziwei Liu, Xiaoxiao Li, and Chen Change Loy. 2020. Knowledge distillation meets self-supervision. In ECCV."},{"key":"e_1_3_1_67_2","first-page":"18409","volume-title":"CVPR","author":"Xu Yinghao","year":"2022","unstructured":"Yinghao Xu, Sida Peng, Ceyuan Yang, Yujun Shen, and Bolei Zhou. 2022. 3D-aware image synthesis via learning structural and textural representations. In CVPR. 18409\u201318418."},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3571857"},{"key":"e_1_3_1_69_2","first-page":"18419","volume-title":"CVPR","author":"Xue Yang","year":"2022","unstructured":"Yang Xue, Yuheng Li, Krishna Kumar Singh, and Yong Jae Lee. 2022. GIRAFFE HD: A high-resolution 3D-aware generative model. In CVPR. 18419\u201318428."},{"key":"e_1_3_1_70_2","first-page":"12803","volume-title":"CVPR","author":"Yenamandra Tarun","year":"2021","unstructured":"Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. 2021. i3DMM: Deep implicit 3d morphable model of human heads. In CVPR. 12803\u201312813."},{"key":"e_1_3_1_71_2","first-page":"325","volume-title":"ECCV","author":"Yu Changqian","year":"2018","unstructured":"Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. 2018. BiSeNet: Bilateral segmentation network for real-time semantic segmentation. In ECCV. 325\u2013341."},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3514248"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"e_1_3_1_74_2","first-page":"18429","volume-title":"CVPR","author":"Zhang Xuanmeng","year":"2022","unstructured":"Xuanmeng Zhang, Zhedong Zheng, Daiheng Gao, Bang Zhang, Pan Pan, and Yi Yang. 2022. Multi-view consistent generative adversarial networks for 3D-aware image synthesis. In CVPR. IEEE, 18429\u201318438."},{"key":"e_1_3_1_75_2","first-page":"658","volume-title":"ECCV","author":"Zhang Youcai","year":"2020","unstructured":"Youcai Zhang, Zhonghao Lan, Yuchen Dai, Fangao Zeng, Yan Bai, Jie Chang, and Yichen Wei. 2020. Prime-aware adaptive distillation. In ECCV. 658\u2013674."},{"key":"e_1_3_1_76_2","article-title":"CIPS-3D: A 3D-aware generator of GANs based on conditionally-independent pixel synthesis","volume":"2110","author":"Zhou Peng","year":"2021","unstructured":"Peng Zhou, Lingxi Xie, Bingbing Ni, and Qi Tian. 2021. CIPS-3D: A 3D-aware generator of GANs based on conditionally-independent pixel synthesis. CoRR abs\/2110.09788 (2021).","journal-title":"CoRR"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3635717","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3635717","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:56:59Z","timestamp":1750291019000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3635717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,8]]},"references-count":75,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3635717"],"URL":"https:\/\/doi.org\/10.1145\/3635717","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,8]]},"assertion":[{"value":"2023-07-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-21","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}