High-Quality Face Caricature via Style Translation
Lamyanba Laishram Muhammad Shaheryar Jong Taek Lee Soon Ki Jung
Caricature is an exaggerated form of artistic portraiture that accentuates
unique yet subtle characteristics of human faces. Recently, advancements in
deep end-to-end techniques have yielded encouraging outcomes in capturing both
style and elevated exaggerations in creating face caricatures. Most of these
approaches tend to produce cartoon-like results that could be more practical
for real-world applications. In this study, we proposed a high-quality,
unpaired face caricature method that is appropriate for use in the real world
and uses computer vision techniques and GAN models. We attain the exaggeration
of facial features and the stylization of appearance through a two-step
process: Face caricature generation and face caricature projection. The face
caricature generation step creates new caricature face datasets from real
images and trains a generative model using the real and newly created
caricature datasets. The Face caricature projection employs an encoder trained
with real and caricature faces with the pretrained generator to project real
and caricature faces. We perform an incremental facial exaggeration from the
real image to the caricature faces using the encoder and generator's latent
space. Our projection preserves the facial identity, attributes, and
expressions from the input image. Also, it accounts for facial occlusions, such
as reading glasses or sunglasses, to enhance the robustness of our model.
Furthermore, we conducted a comprehensive comparison of our approach with
various state-of-the-art face caricature methods, highlighting our process's
distinctiveness and exceptional realism.