Content-Conditioned Generation of Stylized Free hand Sketches
Jiajun Liu Siyuan Wang Guangming Zhu Liang Zhang Ning Li Eryang Gao
In recent years, the recognition of free-hand sketches has remained a popular
task. However, in some special fields such as the military field, free-hand
sketches are difficult to sample on a large scale. Common data augmentation and
image generation techniques are difficult to produce images with various
free-hand sketching styles. Therefore, the recognition and segmentation tasks
in related fields are limited. In this paper, we propose a novel adversarial
generative network that can accurately generate realistic free-hand sketches
with various styles. We explore the performance of the model, including using
styles randomly sampled from a prior normal distribution to generate images
with various free-hand sketching styles, disentangling the painters' styles
from known free-hand sketches to generate images with specific styles, and
generating images of unknown classes that are not in the training set. We
further demonstrate with qualitative and quantitative evaluations our
advantages in visual quality, content accuracy, and style imitation on
SketchIME.