  Multi-view face synthesis from a single image is an ill-posed problem and
often suffers from serious appearance distortion. Producing photo-realistic and
identity preserving multi-view results is still a not well defined synthesis
problem. This paper proposes Load Balanced Generative Adversarial Networks
(LB-GAN) to precisely rotate the yaw angle of an input face image to any
specified angle. LB-GAN decomposes the challenging synthesis problem into two
well constrained subtasks that correspond to a face normalizer and a face
editor respectively. The normalizer first frontalizes an input image, and then
the editor rotates the frontalized image to a desired pose guided by a remote
code. In order to generate photo-realistic local details, the normalizer and
the editor are trained in a two-stage manner and regulated by a conditional
self-cycle loss and an attention based L2 loss. Exhaustive experiments on
controlled and uncontrolled environments demonstrate that the proposed method
not only improves the visual realism of multi-view synthetic images, but also
preserves identity information well.
