Abstract
Recently, GAN-based manipulation methods have been proposed to effectively edit and transfer facial attributes. However, these state-of-the-art methods usually fail to delicately manipulate hair attributes because hair does not own a concrete shape and varies a lot with flexible structure. Therefore, how to achieve high-fidelity hair attribute transfer becomes a challenging task. In this paper, we propose a coarse-to-fine cascaded feature mapping network (CFMNet), which can disentangle hair into coarse-grained and fine-grained attributes, and transform hair feature in latent space according to a reference image. The disentangled hair attributes consist of the coarse-grained labels, including length, waviness and bangs, and the fine-grained 3D model, including geometry and color. Next we design a cascaded feature mapping network to manipulate the attributes in a coarse-to-fine way between source and reference images, which can adjust and control hair feature more delicately. Moreover, we also construct an identity loss to avoid the destruction of identity information in source image. A variety of experimental results demonstrate the effectiveness of our proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdal, R., Qin, Y., Wonka, P.: Image2stylegan: how to embed images into the stylegan latent space? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4432–4441 (2019)
Abdal, R., Zhu, P., Mitra, N.J., Wonka, P.: Styleflow: attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Trans. Graph. (TOG) 40(3), 1–21 (2021)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194 (1999)
Chai, M., Shao, T., Wu, H., Weng, Y., Zhou, K.: Autohair: Fully automatic hair modeling from a single image. ACM Trans. Graph. 35(4) (2016)
Chai, M., Wang, L., Weng, Y., Yu, Y., Guo, B., Zhou, K.: Single-view hair modeling for portrait manipulation. ACM Trans. Graph. (TOG) 31(4), 1–8 (2012)
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789–8797 (2018)
Goodfellow, I.J., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)
He, Z., Zuo, W., Kan, M., Shan, S., Chen, X.: Attgan: facial attribute editing by only changing what you want. IEEE Trans. Image Process. 28(11), 5464–5478 (2019)
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. arXiv preprint arXiv:1706.08500 (2017)
Hu, L., Ma, C., Luo, L., Li, H.: Single-view hair modeling using a hairstyle database. ACM Trans. Graph. (ToG) 34(4), 1–9 (2015)
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, pp. 1125–1134 (2017)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)
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, pp. 8110–8119 (2020)
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, pp. 5549–5558 (2020)
Liu, M., Ding, Y., Xia, M., Liu, X., Ding, E., Zuo, W., Wen, S.: Stgan: a unified selective transfer network for arbitrary image attribute editing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3673–3682 (2019)
Luo, L., Li, H., Rusinkiewicz, S.: Structure-aware hair capture. ACM Trans. Graph. (TOG) 32(4), 1–12 (2013)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
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. arXiv preprint arXiv:2010.16417 (2020)
Tewari, A., et al.: Stylerig: rigging stylegan for 3d control over portrait images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6142–6151 (2020)
Zhou, Y., et al.: Hairnet: single-view hair reconstruction using convolutional neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 235–251 (2018)
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, pp. 2223–2232 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Xie, Z., Zhang, G., Yu, C., Zheng, J., Sheng, B. (2021). CFMNet: Coarse-to-Fine Cascaded Feature Mapping Network for Hair Attribute Transfer. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-89029-2_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89028-5
Online ISBN: 978-3-030-89029-2
eBook Packages: Computer ScienceComputer Science (R0)