  Facial attribute editing aims to manipulate single or multiple attributes of
a face image, i.e., to generate a new face with desired attributes while
preserving other details. Recently, generative adversarial net (GAN) and
encoder-decoder architecture are usually incorporated to handle this task with
promising results. Based on the encoder-decoder architecture, facial attribute
editing is achieved by decoding the latent representation of the given face
conditioned on the desired attributes. Some existing methods attempt to
establish an attribute-independent latent representation for further attribute
editing. However, such attribute-independent constraint on the latent
representation is excessive because it restricts the capacity of the latent
representation and may result in information loss, leading to over-smooth and
distorted generation. Instead of imposing constraints on the latent
representation, in this work we apply an attribute classification constraint to
the generated image to just guarantee the correct change of desired attributes,
i.e., to "change what you want". Meanwhile, the reconstruction learning is
introduced to preserve attribute-excluding details, in other words, to "only
change what you want". Besides, the adversarial learning is employed for
visually realistic editing. These three components cooperate with each other
forming an effective framework for high quality facial attribute editing,
referred as AttGAN. Furthermore, our method is also directly applicable for
attribute intensity control and can be naturally extended for attribute style
manipulation. Experiments on CelebA dataset show that our method outperforms
the state-of-the-arts on realistic attribute editing with facial details well
preserved.
