Abstract
Despite the significant success in image-to-image translation and latent representation based facial attribute editing and expression synthesis, the existing approaches still have limitations of preserving the identity and sharpness of details, and generating distinct image translations. To address these issues, we propose a Texture Deformation Based GAN, namely TDB-GAN, to disentangle texture from original image. The disentangled texture is used to transfer facial attributes and expressions before the deformation to target shape and poses. Sharper details and more distinct visual effects are observed in the synthesized faces. In addition, it brings faster convergence during training. In the extensive ablation studies, we also evaluate our method qualitatively and quantitatively on facial attribute and expression synthesis. The results on both the CelebA and RaFD datasets suggest that TDB-GAN achieves better performance.
The work is supported by National Natural Science Foundation of China (Grant No. 61672357 and U1713214), and the Science and Technology Project of Guangdong Province (Grant No. 2018A050501014).
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Chen, W., Xie, X., Jia, X., Shen, L. (2019). Texture Deformation Based Generative Adversarial Networks for Multi-domain Face Editing. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_21
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