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Despite the significant success in image-to-image translation and latentrepresentation based facial attribute editing and expression synthesis, theexisting 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 disentangletexture from original image and transfers domains based on the extractedtexture. The approach utilizes the texture to transfer facial attributes andexpressions without the consideration of the object pose. This leads to shaperdetails and more distinct visual effect of the synthesized faces. In addition,it brings the faster convergence during training. The effectiveness of theproposed method is validated through extensive ablation studies. We alsoevaluate our approach qualitatively and quantitatively on facial attribute andfacial expression synthesis. The results on both the CelebA and RaFD datasetssuggest that Texture Deformation Based GAN achieves better performance.

Texture Deformation Based Generative Adversarial Networks for Face Editing