  In this paper, we explore the task of generating photo-realistic face images
from lines. Previous methods based on conditional generative adversarial
networks (cGANs) have shown their power to generate visually plausible images
when a conditional image and an output image share well-aligned structures.
However, these models fail to synthesize face images with a whole set of
well-defined structures, e.g. eyes, noses, mouths, etc., especially when the
conditional line map lacks one or several parts. To address this problem, we
propose a conditional self-attention generative adversarial network (CSAGAN).
We introduce a conditional self-attention mechanism to cGANs to capture
long-range dependencies between different regions in faces. We also build a
multi-scale discriminator. The large-scale discriminator enforces the
completeness of global structures and the small-scale discriminator encourages
fine details, thereby enhancing the realism of generated face images. We
evaluate the proposed model on the CelebA-HD dataset by two perceptual user
studies and three quantitative metrics. The experiment results demonstrate that
our method generates high-quality facial images while preserving facial
structures. Our results outperform state-of-the-art methods both quantitatively
and qualitatively.
