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EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13676))

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Abstract

Most existing methods view makeup transfer as transferring color distributions of different facial regions and ignore details such as eye shadows and blushes. Besides, they only achieve controllable transfer within predefined fixed regions. This paper emphasizes the transfer of makeup details and steps towards more flexible controls. To this end, we propose Exquisite and locally editable GAN for makeup transfer (EleGANt). It encodes facial attributes into pyramidal feature maps to preserves high-frequency information. It uses attention to extract makeup features from the reference and adapt them to the source face, and we introduce a novel Sow-Attention Module that applies attention within shifted overlapped windows to reduce the computational cost. Moreover, EleGANt is the first to achieve customized local editing within arbitrary areas by corresponding editing on the feature maps. Extensive experiments demonstrate that EleGANt generates realistic makeup faces with exquisite details and achieves state-of-the-art performance. The code is available at https://github.com/Chenyu-Yang-2000/EleGANt.

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References

  1. An, J., Xiong, H., Huan, J., Luo, J.: Ultrafast photorealistic style transfer via neural architecture search. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 10443–10450 (2020)

    Google Scholar 

  2. Cai, M., Zhang, H., Huang, H., Geng, Q., Li, Y., Huang, G.: Frequency domain image translation: more photo-realistic, better identity-preserving. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 13930–13940 (2021)

    Google Scholar 

  3. Chang, H., Lu, J., Yu, F., Finkelstein, A.: PairedCycleGAN: asymmetric style transfer for applying and removing makeup. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 40–48 (2018)

    Google Scholar 

  4. Chen, C.F., Panda, R., Fan, Q.: RegionViT: regional-to-local attention for vision transformers. In: Proceedings of the International Conference on Learning Representations (ICLR) (2022)

    Google Scholar 

  5. Chen, H.J., Hui, K.M., Wang, S.Y., Tsao, L.W., Shuai, H.H., Cheng, W.H.: BeautyGlow: on-demand makeup transfer framework with reversible generative network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10042–10050 (2019)

    Google Scholar 

  6. Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: StarGAN v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8188–8197 (2020)

    Google Scholar 

  7. Chu, X., et al.: Twins: revisiting the design of spatial attention in vision transformers. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS), pp. 9355–9366 (2021)

    Google Scholar 

  8. Deng, H., Han, C., Cai, H., Han, G., He, S.: Spatially-invariant style-codes controlled makeup transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6549–6557 (2021)

    Google Scholar 

  9. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)

    Google Scholar 

  10. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414–2423 (2016)

    Google Scholar 

  11. Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS) (2014)

    Google Scholar 

  12. Gu, Q., Wang, G., Chiu, M.T., Tai, Y.W., Tang, C.K.: LADN: local adversarial disentangling network for facial makeup and de-makeup. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10481–10490 (2019)

    Google Scholar 

  13. Guo, D., Sim, T.: Digital face makeup by example. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 73–79 (2009)

    Google Scholar 

  14. Heo, B., Yun, S., Han, D., Chun, S., Choe, J., Oh, S.J.: Rethinking spatial dimensions of vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 11936–11945 (2021)

    Google Scholar 

  15. Ho, J., Kalchbrenner, N., Weissenborn, D., Salimans, T.: Axial attention in multidimensional transformers. arXiv preprint arXiv:1912.12180 (2019)

  16. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1501–1510 (2017)

    Google Scholar 

  17. Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS), pp. 2017–2025 (2015)

    Google Scholar 

  18. Jiang, W., et al.: PSGAN: pose and expression robust spatial-aware GAN for customizable makeup transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5194–5202 (2020)

    Google Scholar 

  19. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 694–711 (2016)

    Google Scholar 

  20. Kim, S.S., Kolkin, N., Salavon, J., Shakhnarovich, G.: Deformable style transfer. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 246–261 (2020)

    Google Scholar 

  21. Li, C., Zhou, K., Lin, S.: Simulating makeup through physics-based manipulation of intrinsic image layers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4621–4629 (2015)

    Google Scholar 

  22. Li, T., et al.: BeautyGAN: instance-level facial makeup transfer with deep generative adversarial network. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 645–653 (2018)

    Google Scholar 

  23. Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual attribute transfer through deep image analogy. ACM Trans. Graph. 36(4), 1–15 (2017)

    Article  Google Scholar 

  24. Liu, L., Xing, J., Liu, S., Xu, H., Zhou, X., Yan, S.: Wow! You are so beautiful today! ACM Trans. Multim. Comput. Commun. Appl. (TOMM) 11(1s), 1–22 (2014)

    Google Scholar 

  25. Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10012–10022 (2021)

    Google Scholar 

  26. Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4990–4998 (2017)

    Google Scholar 

  27. Parmar, N., et al.: Image transformer. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 4055–4064 (2018)

    Google Scholar 

  28. Rakhimov, R., Volkhonskiy, D., Artemov, A., Zorin, D., Burnaev, E.: Latent video transformer. In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), pp. 101–112 (2021)

    Google Scholar 

  29. Shi, B., Yang, M., Wang, X., Lyu, P., Yao, C., Bai, X.: Aster: an attentional scene text recognizer with flexible rectification. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2035–2048 (2018)

    Article  Google Scholar 

  30. Tong, W.S., Tang, C.K., Brown, M.S., Xu, Y.Q.: Example-based cosmetic transfer. In: Proceedings of the 15th Pacific Conference on Computer Graphics and Applications (PG), pp. 211–218 (2007)

    Google Scholar 

  31. Vaswani, A., et al.: Attention is all you need. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS), pp. 6000–6010 (2017)

    Google Scholar 

  32. Wan, Z., Chen, H., An, J., Jiang, W., Yao, C., Luo, J.: Facial attribute transformers for precise and robust makeup transfer. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1717–1726 (2022)

    Google Scholar 

  33. Wang, H., Li, Y., Wang, Y., Hu, H., Yang, M.H.: Collaborative distillation for ultra-resolution universal style transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1860–1869 (2020)

    Google Scholar 

  34. Wang, W., et al.: Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 568–578 (2021)

    Google Scholar 

  35. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7794–7803 (2018)

    Google Scholar 

  36. Weissenborn, D., Täckström, O., Uszkoreit, J.: Scaling autoregressive video models. In: Proceedings of the International Conference on Learning Representations (ICLR) (2020)

    Google Scholar 

  37. Wu, C., et al.: Godiva: generating open-domain videos from natural descriptions. arXiv preprint arXiv:2104.14806 (2021)

  38. Wu, C., et al.: N\(\backslash \)” UWA: visual synthesis pre-training for neural visual world creation. arXiv preprint arXiv:2111.12417 (2021)

  39. Xu, K., et al.: Show, attend and tell: Neural image caption generation with visual attention. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 2048–2057 (2015)

    Google Scholar 

  40. Xu, L., Du, Y., Zhang, Y.: An automatic framework for example-based virtual makeup. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 3206–3210 (2013)

    Google Scholar 

  41. Yuan, L., et al.: Tokens-to-token VIT: Training vision transformers from scratch on imageNet. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 558–567 (2021)

    Google Scholar 

  42. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

  43. Zhang, P., et al.: Multi-scale vision longformer: a new vision transformer for high-resolution image encoding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2998–3008 (2021)

    Google Scholar 

  44. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2223–2232 (2017)

    Google Scholar 

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Acknowledgements

This work is supported by the Ministry of Science and Technology of the People´s Republic of China, the 2030 Innovation Megaprojects “Program on New Generation Artificial Intelligence” (Grant No. 2021AAA0150000). This work is also supported by a grant from the Guoqiang Institute, Tsinghua University. Thanks to Steve Lin for his pre-reading and constructive suggestions.

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Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Chenyu Yang, Wanrong He, Yingqing Xu & Yang Gao

  2. Shanghai Qi Zhi Institute, Shanghai, China

    Yang Gao

Authors
  1. Chenyu Yang
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  2. Wanrong He
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  3. Yingqing Xu
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  4. Yang Gao
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Corresponding authors

Correspondence to Yingqing Xu or Yang Gao .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Yang, C., He, W., Xu, Y., Gao, Y. (2022). EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13676. Springer, Cham. https://doi.org/10.1007/978-3-031-19787-1_42

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  • DOI: https://doi.org/10.1007/978-3-031-19787-1_42

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