Multi‐Modal Face Stylization with a Generative Prior
In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high‐quality artistic faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supp...
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| Veröffentlicht in: | Computer graphics forum Jg. 42; H. 7 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Blackwell Publishing Ltd
01.10.2023
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high‐quality artistic faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi‐modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder‐decoder architecture. Specifically, we use the mid‐resolution and high‐resolution layers of StyleGAN as the decoder to generate high‐quality faces, while aligning its low‐resolution layer with the encoder to extract and preserve input facial details. We also introduce a two‐stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces. In the second stage, the entire network is fine‐tuned with artistic data for stylized face generation. To enable the fine‐tuned model to be applied in zero‐shot and one‐shot stylization tasks, we train an additional mapping network from the large‐scale Contrastive‐Language‐Image‐Pre‐training (CLIP) space to a latent w+ space of fine‐tuned StyleGAN. Qualitative and quantitative experiments show that our framework achieves superior performance in both one‐shot and zero‐shot face stylization tasks, outperforming state‐of‐the‐art methods by a large margin. |
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| AbstractList | In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high‐quality artistic faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi‐modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder‐decoder architecture. Specifically, we use the mid‐resolution and high‐resolution layers of StyleGAN as the decoder to generate high‐quality faces, while aligning its low‐resolution layer with the encoder to extract and preserve input facial details. We also introduce a two‐stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces. In the second stage, the entire network is fine‐tuned with artistic data for stylized face generation. To enable the fine‐tuned model to be applied in zero‐shot and one‐shot stylization tasks, we train an additional mapping network from the large‐scale Contrastive‐Language‐Image‐Pre‐training (CLIP) space to a latent w+ space of fine‐tuned StyleGAN. Qualitative and quantitative experiments show that our framework achieves superior performance in both one‐shot and zero‐shot face stylization tasks, outperforming state‐of‐the‐art methods by a large margin. |
| Author | Dong, Yi Huang, Haibin Ma, Chongyang Li, Mengtian Lin, Minxuan Wan, Pengfei |
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| Cites_doi | 10.1109/TVCG.2021.3114308 10.1109/CVPR52688.2022.01042 10.1145/3450626.3459771 10.1145/3550454.3555437 10.1109/CVPR.2018.00068 10.1109/CVPR.2019.00482 10.1109/CVPR42600.2020.00832 10.1109/CVPR52688.2022.01753 10.1007/978-3-031-19778-9_40 10.1109/CVPR46437.2021.01060 10.1109/ICCV.2017.167 10.1109/ICCV48922.2021.00209 10.1109/CVPR52688.2022.01048 10.1007/s11263-019-01284-z 10.1109/ICCV.2019.00453 10.1109/CVPR52729.2023.00442 10.1109/CVPR.2019.01100 10.1007/978-3-031-25056-9_14 10.1109/ICCV48922.2021.00664 10.1145/3450626.3459860 10.1109/TMM.2021.3113786 10.1145/3306346.3322984 10.1109/CVPR46437.2021.00232 10.1109/CVPR42600.2020.00935 10.1109/ICCV48922.2021.00951 10.1145/3528223.3530164 10.1007/978-3-031-19787-1_8 10.1109/CVPR42600.2020.00821 10.1109/ICCV48922.2021.01368 10.1109/TPAMI.2023.3283551 10.1109/CVPR42600.2020.00813 10.1145/3450626.3459838 10.1109/CVPR52688.2022.00754 10.1109/CVPR.2019.00453 |
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| References | 2021; 24 2023 2022 2021 2020 2021; 29 2019 2019; 38 2018 2020; 128 2017 2022; 41 2021; 40 e_1_2_7_5_2 e_1_2_7_3_2 e_1_2_7_9_2 e_1_2_7_7_2 e_1_2_7_19_2 e_1_2_7_17_2 e_1_2_7_15_2 e_1_2_7_13_2 e_1_2_7_41_2 e_1_2_7_11_2 e_1_2_7_43_2 e_1_2_7_45_2 e_1_2_7_47_2 e_1_2_7_26_2 e_1_2_7_49_2 e_1_2_7_28_2 e_1_2_7_50_2 e_1_2_7_25_2 e_1_2_7_52_2 e_1_2_7_23_2 e_1_2_7_31_2 e_1_2_7_54_2 e_1_2_7_21_2 e_1_2_7_33_2 e_1_2_7_35_2 e_1_2_7_37_2 e_1_2_7_39_2 e_1_2_7_4_2 e_1_2_7_2_2 e_1_2_7_8_2 e_1_2_7_6_2 e_1_2_7_18_2 e_1_2_7_16_2 e_1_2_7_14_2 e_1_2_7_40_2 e_1_2_7_12_2 e_1_2_7_42_2 e_1_2_7_10_2 e_1_2_7_44_2 e_1_2_7_46_2 e_1_2_7_48_2 e_1_2_7_27_2 e_1_2_7_29_2 e_1_2_7_24_2 e_1_2_7_30_2 e_1_2_7_51_2 e_1_2_7_22_2 e_1_2_7_32_2 e_1_2_7_53_2 e_1_2_7_20_2 e_1_2_7_34_2 e_1_2_7_55_2 e_1_2_7_36_2 Radford A. (e_1_2_7_38_2) 2021 |
| References_xml | – start-page: 8296 year: 2020 end-page: 8305 – start-page: 4690 year: 2019 end-page: 4699 – start-page: 201 year: 2023 end-page: 217 – start-page: 8748 year: 2021 end-page: 8763 article-title: Learning transferable visual models from natural language supervision – start-page: 10684 year: 2022 end-page: 10695 – volume: 40 start-page: 1 issue: 4 year: 2021 end-page: 13 article-title: Agilegan: stylizing portraits by inversion-consistent transfer learning publication-title: ACM Transactions on Graphics (TOG) – start-page: 6711 year: 2021 end-page: 6720 – start-page: 8780 year: 2021 end-page: 8794 – start-page: 10743 year: 2021 end-page: 10752 – start-page: 18062 year: 2022 end-page: 18071 – volume: 40 start-page: 1 issue: 4 year: 2021 end-page: 16 article-title: Stylecarigan: caricature generation via stylegan feature map modulation publication-title: ACM Transactions on Graphics (TOG) – year: 2021 – volume: 41 start-page: 1 issue: 6 year: 2022 end-page: 15 article-title: Vtoonify: Controllable high-resolution portrait video style transfer publication-title: ACM Transactions on Graphics (TOG) – start-page: 1501 year: 2017 end-page: 1510 – start-page: 29710 year: 2021 end-page: 29722 – start-page: 10743 year: 2019 end-page: 10752 – volume: 40 start-page: 1 issue: 4 year: 2021 end-page: 14 article-title: Designing an encoder for stylegan image manipulation publication-title: ACM Transactions on Graphics (TOG) – start-page: 10748 year: 2022 end-page: 10757 – start-page: 4432 year: 2019 end-page: 4441 – start-page: 13718 year: 2022 end-page: 13730 – start-page: 37297 year: 2022 end-page: 37308 – start-page: 7693 year: 2022 end-page: 7702 – year: 2018 – start-page: 218 year: 2018 end-page: 234 – start-page: 172 year: 2018 end-page: 189 – volume: 29 start-page: 1371 issue: 2 year: 2021 end-page: 1383 article-title: Exemplar-based 3d portrait stylization publication-title: IEEE Transactions on Visualization and Computer Graphics – start-page: 13921 year: 2021 end-page: 13929 – start-page: 4401 year: 2019 end-page: 4410 – volume: 38 start-page: 1 issue: 4 year: 2019 end-page: 15 article-title: The face of art: landmark detection and geometric style in portraits publication-title: ACM Transactions on Graphics (TOG) – start-page: 8162 year: 2021 end-page: 8171 – start-page: 2085 year: 2021 end-page: 2094 – start-page: 4552 year: 2023 end-page: 4562 – start-page: 2287 year: 2021 end-page: 2296 – start-page: 128 year: 2022 end-page: 152 – volume: 128 start-page: 2402 issue: 10 year: 2020 end-page: 2417 article-title: Drit++: Diverse image-to-image translation via disentangled representations publication-title: International Journal of Computer Vision – start-page: 695 year: 2022 end-page: 711 – volume: 24 start-page: 4077 year: 2021 end-page: 4091 article-title: Anigan: Style-guided generative adversarial networks for unsupervised anime face generation publication-title: IEEE Transactions on Multimedia – year: 2022 – year: 2020 – start-page: 8110 year: 2020 end-page: 8119 – year: 2023 – start-page: 9332 year: 2020 end-page: 9341 – start-page: 8188 year: 2020 end-page: 8197 – year: 2017 – volume: 41 issue: 4 year: 2022 article-title: Stylegan-nada: Clip-guided domain adaptation of image generators publication-title: ACM Transactions on Graphics (TOG) – start-page: 9650 year: 2021 end-page: 9660 – ident: e_1_2_7_16_2 doi: 10.1109/TVCG.2021.3114308 – ident: e_1_2_7_35_2 doi: 10.1109/CVPR52688.2022.01042 – ident: e_1_2_7_55_2 – start-page: 8748 volume-title: International Conference on Machine Learning (ICML) year: 2021 ident: e_1_2_7_38_2 – ident: e_1_2_7_39_2 doi: 10.1145/3450626.3459771 – ident: e_1_2_7_46_2 doi: 10.1145/3550454.3555437 – ident: e_1_2_7_52_2 doi: 10.1109/CVPR.2018.00068 – ident: e_1_2_7_24_2 – ident: e_1_2_7_32_2 – ident: e_1_2_7_10_2 doi: 10.1109/CVPR.2019.00482 – ident: e_1_2_7_50_2 – ident: e_1_2_7_54_2 – ident: e_1_2_7_15_2 – ident: e_1_2_7_5_2 doi: 10.1109/CVPR42600.2020.00832 – ident: e_1_2_7_22_2 doi: 10.1109/CVPR52688.2022.01753 – ident: e_1_2_7_6_2 doi: 10.1007/978-3-031-19778-9_40 – ident: e_1_2_7_31_2 doi: 10.1109/CVPR46437.2021.01060 – ident: e_1_2_7_29_2 – ident: e_1_2_7_51_2 – ident: e_1_2_7_53_2 – ident: e_1_2_7_13_2 doi: 10.1109/ICCV.2017.167 – ident: e_1_2_7_33_2 doi: 10.1109/ICCV48922.2021.00209 – ident: e_1_2_7_41_2 doi: 10.1109/CVPR52688.2022.01048 – ident: e_1_2_7_26_2 doi: 10.1007/s11263-019-01284-z – ident: e_1_2_7_4_2 doi: 10.1109/ICCV.2019.00453 – ident: e_1_2_7_2_2 doi: 10.1109/CVPR52729.2023.00442 – ident: e_1_2_7_47_2 doi: 10.1109/CVPR.2019.01100 – ident: e_1_2_7_30_2 – ident: e_1_2_7_37_2 doi: 10.1007/978-3-031-25056-9_14 – ident: e_1_2_7_27_2 – ident: e_1_2_7_3_2 doi: 10.1109/ICCV48922.2021.00664 – ident: e_1_2_7_43_2 – ident: e_1_2_7_18_2 doi: 10.1145/3450626.3459860 – ident: e_1_2_7_49_2 – ident: e_1_2_7_28_2 doi: 10.1109/TMM.2021.3113786 – ident: e_1_2_7_48_2 doi: 10.1145/3306346.3322984 – ident: e_1_2_7_34_2 doi: 10.1109/CVPR46437.2021.00232 – ident: e_1_2_7_42_2 doi: 10.1109/CVPR42600.2020.00935 – ident: e_1_2_7_8_2 doi: 10.1109/ICCV48922.2021.00951 – ident: e_1_2_7_12_2 doi: 10.1145/3528223.3530164 – ident: e_1_2_7_14_2 – ident: e_1_2_7_11_2 – ident: e_1_2_7_7_2 doi: 10.1007/978-3-031-19787-1_8 – ident: e_1_2_7_25_2 – ident: e_1_2_7_9_2 doi: 10.1109/CVPR42600.2020.00821 – ident: e_1_2_7_19_2 doi: 10.1109/ICCV48922.2021.01368 – ident: e_1_2_7_23_2 doi: 10.1109/TPAMI.2023.3283551 – ident: e_1_2_7_17_2 – ident: e_1_2_7_21_2 doi: 10.1109/CVPR42600.2020.00813 – ident: e_1_2_7_36_2 – ident: e_1_2_7_40_2 doi: 10.1145/3450626.3459838 – ident: e_1_2_7_44_2 – ident: e_1_2_7_45_2 doi: 10.1109/CVPR52688.2022.00754 – ident: e_1_2_7_20_2 doi: 10.1109/CVPR.2019.00453 |
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| Title | Multi‐Modal Face Stylization with a Generative Prior |
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