Authors: Bahri Batuhan Bilecen, Ahmet Berke Gokmen, Furkan Guzelant, Aysegul Dundar
Abstract: 3D head stylization transforms realistic facial features into artistic
representations, enhancing user engagement across gaming and virtual reality
applications. While 3D-aware generators have made significant advancements,
many 3D stylization methods primarily provide near-frontal views and struggle
to preserve the unique identities of original subjects, often resulting in
outputs that lack diversity and individuality. This paper addresses these
challenges by leveraging the PanoHead model, synthesizing images from a
comprehensive 360-degree perspective. We propose a novel framework that employs
negative log-likelihood distillation (LD) to enhance identity preservation and
improve stylization quality. By integrating multi-view grid score and mirror
gradients within the 3D GAN architecture and introducing a score rank weighing
technique, our approach achieves substantial qualitative and quantitative
improvements. Our findings not only advance the state of 3D head stylization
but also provide valuable insights into effective distillation processes
between diffusion models and GANs, focusing on the critical issue of identity
preservation. Please visit the https://three-bee.github.io/head_stylization for
more visuals.
Source: http://arxiv.org/abs/2411.13536v1
