  Sketching is an intuitive and effective way for content creation. While
significant progress has been made for photorealistic image generation by using
generative adversarial networks, it remains challenging to take a fine-grained
control on synthetic content. The instance normalization layer, which is widely
adopted in existing image translation networks, washes away details in the
input sketch and leads to loss of precise control on the desired shape of the
generated face images. In this paper, we comprehensively investigate the effect
of instance normalization on generating photorealistic face images from
hand-drawn sketches. We first introduce a visualization approach to analyze the
feature embedding for sketches with a group of specific changes. Based on the
visual analysis, we modify the instance normalization layers in the baseline
image translation model. We elaborate a new set of hand-drawn sketches with 11
categories of specially designed changes and conduct extensive experimental
analysis. The results and user studies demonstrate that our method markedly
improve the quality of synthesized images and the conformance with user
intention.
