Face Cartoonisation For Various Poses Using StyleGAN
Kushal Jain Ankith Varun J Anoop Namboodiri
This paper presents an innovative approach to achieve face cartoonisation
while preserving the original identity and accommodating various poses. Unlike
previous methods in this field that relied on conditional-GANs, which posed
challenges related to dataset requirements and pose training, our approach
leverages the expressive latent space of StyleGAN. We achieve this by
introducing an encoder that captures both pose and identity information from
images and generates a corresponding embedding within the StyleGAN latent
space. By subsequently passing this embedding through a pre-trained generator,
we obtain the desired cartoonised output. While many other approaches based on
StyleGAN necessitate a dedicated and fine-tuned StyleGAN model, our method
stands out by utilizing an already-trained StyleGAN designed to produce
realistic facial images. We show by extensive experimentation how our encoder
adapts the StyleGAN output to better preserve identity when the objective is
cartoonisation.