  In this paper, we present an Attention-based Identity Preserving Generative
Adversarial Network (AIP-GAN) to overcome the identity leakage problem from a
source image to a generated face image, an issue that is encountered in a
cross-subject facial expression transfer and synthesis process. Our key insight
is that the identity preserving network should be able to disentangle and
compose shape, appearance, and expression information for efficient facial
expression transfer and synthesis. Specifically, the expression encoder of our
AIP-GAN disentangles the expression information from the input source image by
predicting its facial landmarks using our supervised spatial and channel-wise
attention module. Similarly, the disentangled expression-agnostic identity
features are extracted from the input target image by inferring its combined
intrinsic-shape and appearance image employing our self-supervised spatial and
channel-wise attention mod-ule. To leverage the expression and identity
information encoded by the intermediate layers of both of our encoders, we
combine these features with the features learned by the intermediate layers of
our decoder using a cross-encoder bilinear pooling operation. Experimental
results show the promising performance of our AIP-GAN based technique.
