ELEGANT: Exchanging Latent Encodings with
GAN for Transferring Multiple Face Attributes
Abstract. Recent studies on face attribute transfer have achieved great
success. A lot of models are able to transfer face attributes with an input image. However, they suffer from three limitations: (1) incapability
of generating image by exemplars; (2) being unable to transfer multiple
face attributes simultaneously; (3) low quality of generated images, such
as low-resolution or artifacts. To address these limitations, we propose
a novel model which receives two images of opposite attributes as inputs. Our model can transfer exactly the same type of attributes from
one image to another by exchanging certain part of their encodings. All
the attributes are encoded in a disentangled manner in the latent space,
which enables us to manipulate several attributes simultaneously. Besides, our model learns the residual images so as to facilitate training
on higher resolution images. With the help of multi-scale discriminators for adversarial training, it can even generate high-quality images
with finer details and less artifacts. We demonstrate the effectiveness of
our model on overcoming the above three limitations by comparing with
other methods on the CelebA face database. A pytorch implementation
is available at https://github.com/Prinsphield/ELEGANT.