  Recent studies on face attribute editing by exemplars have achieved promising
results due to the increasing power of deep convolutional networks and
generative adversarial networks. These methods encode attribute-related
information in images into the predefined region of the latent feature space by
employing a pair of images with opposite attributes as input to train model,
the face attribute transfer between the input image and the exemplar can be
achieved by exchanging their attribute-related latent feature region. However,
they suffer from three limitations: (1) the model must be trained using a pair
of images with opposite attributes as input; (2) weak capability of editing
multiple attributes by exemplars; (3) poor quality of generating image. Instead
of imposing opposite-attribute constraints on the input image in order to make
the attribute information of images be encoded in the predefined region of the
latent feature space, in this work we directly apply the attribute labels
constraint to the predefined region of the latent feature space. Meanwhile, an
attribute classification loss is employed to make the model learn to extract
the attribute-related information of images into the predefined latent feature
region of the corresponding attribute, which enables our method to transfer
multiple attributes of the exemplar simultaneously. Besides, a novel model
structure is designed to enhance attribute transfer capabilities by exemplars
while improve the quality of the generated image. Experiments demonstrate the
effectiveness of our model on overcoming the above three limitations by
comparing with other methods on the CelebA dataset.
