  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 in the sharpness of details,
distinct image translation and identity preservation. To address these issues,
we propose a Texture Deformation Based GAN, namely TDB-GAN, to disentangle
texture from original image and transfers domains based on the extracted
texture. The approach utilizes the texture to transfer facial attributes and
expressions without the consideration of the object pose. This leads to shaper
details and more distinct visual effect of the synthesized faces. In addition,
it brings the faster convergence during training. The effectiveness of the
proposed method is validated through extensive ablation studies. We also
evaluate our approach qualitatively and quantitatively on facial attribute and
facial expression synthesis. The results on both the CelebA and RaFD datasets
suggest that Texture Deformation Based GAN achieves better performance.
