AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Computational Visual Media Article
PDF (8.9 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Towards harmonized regional style transfer and manipulation for facial images

School of Mathematics, Jilin University, Changchun 130012, China
School of Artificial Intelligence, Jilin University, Changchun 130012, China
AI Lab, Tencent, Shenzhen 518054, China
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Show Author Information

Graphical Abstract

Abstract

Regional facial image synthesis conditioned on a semantic mask has achieved great attention in the field of computational visual media. However, the appearances of different regions may be inconsistent with each other after performing regional editing. In this paper, we focus on harmonized regional style transfer for facial images. A multi-scale encoder is proposed for accurate style code extraction. The key part of our work is a multi-region style attention module. It adapts multiple regional style embeddings from a reference image to a target image, to generate a harmonious result. We also propose style mapping networks for multi-modal style synthesis. We further employ an invertible flow model which can serve as mapping network to fine-tune the style code by inverting the code to latent space. Experiments on three widely used face datasets were used to evaluate our model by transferring regional facial appearance between datasets. The results show that our model can reliably perform style transfer and multi-modal manipulation, generating output comparable to the state of the art.

Keywords

face manipulationstyle transfergenerative modelsfacial harmonization

Electronic Supplementary Material

Video
41095_0284_ESM2.mp4
Download File(s)
41095_0284_ESM1.pdf (18.6 MB)

References

[1]
Isola, P.; Zhu, J. -Y.; Zhou, T.; Efros, A. A. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5967–5976, 2017.
Crossref
[2]
Zhu, J.-Y.; Zhang, R.; Pathak, D.; Darrell, T.; Efros, A. A.; Wang, O.; Shechtman, E. Toward multimodal image-to-image translation. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 465–476, 2017.
[3]
Chen, Q.; Koltun, V. Photographic image synthesis with cascaded refinement networks. In: Proceedings of the IEEE International Conference on Computer Vision, 1520–1529, 2017.
Crossref
[4]
Wang, T.-C.; Liu, M.-Y.; Zhu, J.-Y.; Tao, A.; Kautz, J.; Catanzaro, B. High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8798–8807, 2018.
Crossref
[5]
Park, T.; Liu, M.-Y.; Wang, T.-C.; Zhu, J.-Y. Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2332–2341, 2019.
Crossref
[6]
Zhu, Z.; Xu, Z.; You, A.; Bai, X. Semantically multi-modal image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5466–5475, 2020.
Crossref
[7]
Zhu, P.; Abdal, R.; Qin, Y.; Wonka, P. SEAN: Image synthesis with semantic region-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5103–5112, 2020.
Crossref
[8]
Yang, D.; Hong, S.; Jang, Y.; Zhao, T.; Lee, H. Diversity sensitive conditional generative adversarial networks. In: Proceedings of the International Conference on Learning Representations, 2019.
[9]
Gu, S.; Bao, J.; Yang, H.; Chen, D.; Wen, F.; Yuan, L. Mask-guided portrait editing with conditional GANs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3431–3440, 2019.
Crossref
[10]
Lee, C.-H.; Liu, Z.; Wu, L.; Luo, P. MaskGAN: Towards diverse and interactive facial image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5549–5558, 2020.
Crossref
[11]
Wang, M.; Yang, G.-Y.; Li, R.; Liang, R.-Z.; Zhang, S.-H.; Hall, P. M.; Hu, S.-M. Example-guided style-consistent image synthesis from semantic labeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1495–1504, 2019.
Crossref
[12]
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 ComputerVision and Pattern Recognition, 8185–8194, 2020.
Crossref
[13]
Karras, T.; Laine, S.; Aila, T. M. A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog-nition, 4396–4405, 2019.
Crossref
[14]
Karras, T.; Laine, S.; Aittala, M.; Hellsten, J.; Lehtinen, J.; Aila, T. Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8107–8116, 2020.
Crossref
[15]
Kingma, D. P.; Welling, M. Auto-encoding variational bayes. In: Proceedings of the International Conference on Learning Representations, 2014.
[16]
Cun, X.; Pun, C.-M. Improving the harmony of the composite image by spatial-separated attention module. IEEE Transactions on Image Processing Vol. 29, 4759–4771, 2020.
Crossref Google Scholar
[17]
Cong, W. Y.; Zhang, J. F.; Niu, L.; Liu, L.; Ling, Z. X.; Li, W. Y.; Zhang, L. DoveNet: Deep image harmonization via domain verification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8391–8400, 2020.
Crossref
[18]
Tsai, Y.-H.; Shen, X.; Lin, Z.; Sunkavalli, K.; Lu, X.; Yang, M.-H. Deep image harmonization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2799–2807, 2017.
Crossref
[19]
Zhu, J.-Y.; Krahenbuhl, P.; Shechtman, E.; Efros, A. A. Learning a discriminative model for the perception of realism in composite images. In: Proceedings of the IEEE International Conference on Computer Vision, 3943–3951, 2015.
Crossref
[20]
Richardson, E.; Alaluf, Y.; Patashnik, O.; Nitzan, Y.; Azar, Y.; Shapiro, S.; Cohen-Or, D. Encoding in style: A StyleGAN encoder for image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2287–2296, 2021.
Crossref
[21]
Chen, R. T. Q.; Rubanova, Y.; Bettencourt, J.; Duvenaud, D. Neural ordinary differential equations. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, 6572–6583, 2018.
[22]
Grathwohl, W.; Chen, R. T. Q.; Bettencourt, J.; Sutskever, I.; Duvenaud, D. FFJORD: Free-form continuous dynamics for scalable reversible generative models. In: Proceedings of the International Conference on Learning Representations, 2018.
[23]
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, Vol. 3, 2672–2680, 2014.
[24]
Arjovsky, M.; Chintala, S.; Bottou, L. Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, Vol. 70, 214–223, 2017.
[25]
Karras, T.; Aila, T.; Laine, S.; Lehtinen, J. Progressive growing of GANs for improved quality, stability, and variation. In: Proceedings of the International Conference on Learning Representations, 2018.
[26]
Denton, E.; Chintala, S.; Szlam, A.; Fergus, R. Deep generative image models using a Laplacian pyramid of adversarial networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, Vol. 1, 1486–1494, 2015.
[27]
Gulrajani, I.; Ahmed, F.; Arjovsky, M.; Dumoulin, V.; Courville, A. Improved training of wasserstein GANs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 5769–5779, 2017.
[28]
Mao, X.; Li, Q.; Xie, H.; Lau, R. Y.; Wang, Z.; Smolley, S. P. Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, 2813–2821, 2017.
Crossref
[29]
Zhang, H.; Goodfellow, I. J.; Metaxas, D. N.; Odena, A. Self-attention generative adversarial networks. In: Proceedings of the 36th International Conference on Machine Learning, 7354–7363, 2019.
[30]
Portenier, T.; Hu, Q.; Szabo, A.; Bigdeli, S. A.; Favaro, P.; Zwicker, M. Faceshop: Deep sketch-based face image editing. ACM Transactions on Graphics Vol. 37, No. 4, Article No. 99, 2018.
Crossref Google Scholar
[31]
Chen, S.-Y.; Liu, F.-L.; Lai, Y.-K.; Rosin, P. L.; Li, C.; Fu, H.; Gao, L. DeepFaceEditing: Deep face generation and editing with disentangled geometry and appearance control. ACM Transactions on Graphics Vol. 40, No. 4, Article No. 90, 2021.
Crossref Google Scholar
[32]
Tan, Z.; Chai, M.; Chen, D.; Liao, J.; Chu, Q.; Yuan, L.; Tulyakov, S.; Yu, N. MichiGAN: Multi-input-conditioned hair image generation for portrait editing. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 95, 2020.
Crossref Google Scholar
[33]
Huang, Z.; Peng, Y.; Hibino, T.; Zhao, C.; Xie, H.; Fukusato, T.; Miyata, K. DualFace: Two-stage drawing guidance for freehand portrait sketching. Computational Visual Media Vol. 8, No. 1, 63–77, 2022.
Crossref Google Scholar
[34]
Shen, Y. J.; Gu, J. J.; Tang, X. O.; Zhou, B. L. Interpreting the latent space of GANs for semantic face editing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9240–9249, 2020.
Crossref
[35]
Shen, Y. J.; Zhou, B. L. Closed-form factorization of latent semantics in GANs. arXiv preprint arXiv:2007.06600, 2020.
Crossref Google Scholar
[36]
Tewari, A.; Elgharib, M.; Bharaj, G.; Bernard, F.; Seidel, H.-P.; Perez, P.; Zollhofer, M.; Theobalt, C. StyleRig: Rigging StyleGAN for 3D control over portrait images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6141–6150, 2020.
Crossref
[37]
Abdal, R.; Zhu, P. H.; Mitra, N.; Wonka, P. StyleFlow: Attribute-conditioned exploration of StyleGAN-generated images using conditional continuous normalizing flows. arXiv preprint arXiv:2008.02401, 2020.
Crossref Google Scholar
[38]
Rezende, D.; Mohamed, S. Variational inference with normalizing flows. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, Vol. 37, 1530–1538, 2015.
[39]
Zhu, J.; Shen, Y.; Zhao, D.; Zhou, B. In-domain GAN inversion for real image editing. In: Computer Vision – ECCV 2020. Lecture Notes in Computer Science, Vol. 12362. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 592–608, 2020.
[40]
Sun, R. Q.; Huang, C.; Zhu, H. L.; Ma, L. Z. Mask-aware photorealistic facial attribute manipulation. Computational Visual Media Vol. 7, No. 3, 363–374, 2021.
Crossref Google Scholar
[41]
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; Polosukhin, I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 6000–6010, 2017.
[42]
Wang, X.; Girshick, R.; Gupta, A.; He, K. Non-local neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7794–7803, 2018.
Crossref
[43]
Zhang, P.; Zhang, B.; Chen, D.; Yuan, L.; Wen, F. Cross-domain correspondence learning for exemplar-based image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5142–5152, 2020.
Crossref
[44]
Lee, J.; Kim, E.; Lee, Y.; Kim, D.; Chang, J.; Choo, J. Reference-based sketch image colorization using augmented-self reference and dense semantic correspondence. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5800–5809, 2020.
Crossref
[45]
Jiang, W.; Liu, S.; Gao, C.; Cao, J.; He, R.; Feng, J.; Yan, S. 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, 5193–5201, 2020.
Crossref
[46]
Huang, X.; Liu, M. Y.; Belongie, S.; Kautz, J. Multimodal unsupervised image-to-image translation. In: Computer Vision – ECCV 2018. Lecture Notes in Computer Science, Vol. 11207. Ferrari, V.; Hebert, M.; Sminchisescu, C.; Weiss, Y. Eds. Springer Cham, 179–196, 2018.
Crossref
[47]
Lee, H.-Y.; Tseng, H.-Y.; Huang, J.-B.; Singh, M. K.; Yang, M.-H. Diverse image-to-image translation via disentangled representations. In: Proceedings of the European Conference on Computer Vision, 2018.
Crossref
[48]
Cun, X.; Pun, C.-M. Improving the harmony of the composite image by spatial-separated attention module. IEEE Transactions on Image Processing Vol. 29, 4759–4771, 2020.
Crossref Google Scholar
[49]
Yang, G. D.; Huang, X.; Hao, Z. K.; Liu, M. Y.; Belongie, S.; Hariharan, B. PointFlow: 3D point cloud generation with continuous normalizing flows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 4540–4549, 2019.
Crossref
[50]
Chen, L.-C.; Papandreou, G.; Schroff, F.; Adam, H. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.
Crossref Google Scholar
[51]
Liu, Y.; Shi, H.; Shen, H.; Si, Y.; Wang, X.; Mei, T. A new dataset and boundary-attention semantic segmentation for face parsing. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, No. 07, 11637–11644, 2020.
Crossref Google Scholar
[52]
Tan, Z.; Chen, D.; Chu, Q.; Chai, M.; Liao, J.; He, M.; Yuan, L.; Hua, G.; Yu, N. Efficient semantic image synthesis via class-adaptive normalization. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 44, No. 9, 4852–4866, 2022.
Google Scholar
[53]
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; Hochreiter, S. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 6629–6640, 2017.
[54]
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; Wang, O. The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 586–595, 2018.
Crossref
[55]
Miyato, T.; Kataoka, T.; Koyama, M.; Yoshida, Y. Spectral normalization for generative adversarial networks. In: Proceedings of the International Conference on Learning Representations, 2018.
[56]
Kingma, D. P.; Ba, J. L. Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations, 2015.
Computational Visual Media
Volume 9 Issue 2,
June 2023
Pages 351-366
DOI: 10.1007/s41095-022-0284-6
Cite this article:
Wang C, Tang F, Zhang Y, et al. Towards harmonized regional style transfer and manipulation for facial images. Computational Visual Media, 2023, 9(2): 351-366. https://doi.org/10.1007/s41095-022-0284-6

7476

Views

50

Downloads

6

Crossref

5

Web of Science

4

Scopus

0

CSCD

Altmetrics
Received: 18 February 2022
Accepted: 16 March 2022
Published: 03 January 2023
© The Author(s) 2022.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.
Return