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.

本研究解决了现有三维头部风格化方法在保持人物独特身份方面的不足，尤其是在输出的多样性和个性上。提出了一种新颖的框架，通过负对数似然蒸馏来提高身份保持和风格化质量，实现了定性和定量的显著提升。这项研究不仅推进了三维头部风格化的领域，也为扩散模型与GAN之间有效蒸馏过程提供了重要见解。

保持身份的三维头部风格化与多视图评分蒸馏