Japanese Kanji Font Style Transfer based on GAN with Unpaired Training

Interactive global illumination (Preconvolved Radiance Caching), appearance editing tech- niques (Surface Light Field Manipulation, Material Style Transfer, Shape Color Subspaces)

Based on the parametrization of the Bézier curves, the distribution of geometric features (e.g. stroke eccentricity) in the query sketch can be modeled and its SAR distance [SRG15]

As shown in Figure 1, for each input character with any font style, we build a font skeleton manifold learnt from 28 fonts including both printing styles and handwriting styles,

Given a set of training fonts, for each character of each font style, we extract feature vectors to represent its skeleton and outline shape, respectively.. Then, we map these

To illustrate the feasibility of our technique, we performed style transfer experiments and achieved visually plausible results by using a relatively small training data set..

We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs.. Besides the

To com- pute a font specificity score for each font given the collected data, we use an image specificity based method [JP15], except we con- sider the words provided by

Varying this ratio allows to modulate the intensity of the style transfer process: relative low weights of the style term (lower intensities of the style transfer) do not