  Image attribute transfer aims to change an input image to a target one with
expected attributes, which has received significant attention in recent years.
However, most of the existing methods lack the ability to de-correlate the
target attributes and irrelevant information, i.e., the other attributes and
background information, thus often suffering from blurs and artifacts. To
address these issues, we propose a novel Attribute Manifold Encoding GAN
(AME-GAN) for fully-featured attribute transfer, which can modify and adjust
every detail in the images. Specifically, our method divides the input image
into image attribute part and image background part on manifolds, which are
controlled by attribute latent variables and background latent variables
respectively. Through enforcing attribute latent variables to Gaussian
distributions and background latent variables to uniform distributions
respectively, the attribute transfer procedure becomes controllable and image
generation is more photo-realistic. Furthermore, we adopt a conditional
multi-scale discriminator to render accurate and high-quality target attribute
images. Experimental results on three popular datasets demonstrate the
superiority of our proposed method in both performances of the attribute
transfer and image generation quality.
