Abstract
In this paper, we propose a region adaptive makeup transfer GAN, called RamGAN, for precise region-level makeup transfer. Compared to face-level transfer methods, our RamGAN uses spatial-aware Region Attentive Morphing Module (RAMM) to encode Region Attentive Matrices (RAMs) for local regions like lips, eye shadow and skin. After that, the Region Style Injection Module (RSIM) is applied to RAMs produced by RAMM to obtain two Region Makeup Tensors, \(\gamma \) and \(\beta \), which are subsequently added to the feature map of source image to transfer the makeup. As attention and makeup styles are calculated for each region, RamGAN can achieve better disentangled makeup transfer for different facial regions. When there are significant pose and expression variations between source and reference, RamGAN can also achieve better transfer results, due to the integration of spatial information and region-level correspondence. Experimental results are conducted on public datasets like MT, M-Wild and Makeup datasets, both visual and quantitative results and user study suggest that our approach achieves better transfer results than state-of-the-art methods like BeautyGAN, BeautyGlow, DMT, CPM and PSGAN.
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Notes
- 1.
As the source code of BeautyGlow is not available, we directly used the makeup transfer results posted on https://github.com/BeautyGlow/BeautyGlow.github.io for the same source and reference images for comparison.
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Acknowledgements
This research was supported by National Natural Science Foundation of China under grant no. 91959108, and Guangdong Basic and Applied Basic Research Foundation under Grant no. 2020A1515111199 and 2022A1515011018.
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Xiang, J., Chen, J., Liu, W., Hou, X., Shen, L. (2022). RamGAN: Region Attentive Morphing GAN for Region-Level 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 13682. Springer, Cham. https://doi.org/10.1007/978-3-031-20047-2_41
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