Abstract
Face rotation provides an effective and cheap way for data augmentation and representation learning of face recognition. It is a challenging generative learning problem due
to the large pose discrepancy between two face images.
This work focuses on flexible face rotation of arbitrary head
poses, including extreme profile views. We propose a novel Couple-Agent Pose-Guided Generative Adversarial Network (CAPG-GAN) to generate both neutral and profile
head pose face images. The head pose information is encoded by facial landmark heatmaps. It not only forms a mask
image to guide the generator in learning process but also
provides a flexible controllable condition during inference.
A couple-agent discriminator is introduced to reinforce on
the realism of synthetic arbitrary view faces. Besides the
generator and conditional adversarial loss, CAPG-GAN
further employs identity preserving loss and total variation regularization to preserve identity information and re-
fine local textures respectively. Quantitative and qualitative
experimental results on the Multi-PIE and LFW databases consistently show the superiority of our face rotation
method over the state-of-the-art