In the present study, we propose to implement a
new framework for estimating generative models via an
adversarial process to extend an existing GAN framework
and develop a white-box controllable image cartoonization,
which can generate high-quality cartooned images/videos
from real-world photos and videos. The learning purposes
of our system are based on three distinct representations:
surface representation, structure representation, and
texture representation. The surface representation refers to
the smooth surface of the images. The structure
representation relates to the sparse colour blocks and
compresses generic content. The texture representation
shows the texture, curves, and features in cartoon images.
Generative Adversarial Network (GAN) framework
decomposes the images into different representations and
learns from them to generate cartoon images. This
decomposition makes the framework more controllable and
flexible which allows users to make changes based on the
required output. This approach overcomes any previous
system in terms of maintaining clarity, colours, textures,
shapes of images yet showing the characteristics of cartoon
images.
Sparse coding based lip texture representation for visual speaker identification
