用多视角分数蒸馏实现360度保形人脸艺术化，提升个性化表现。
Identity Preserving 3D Head Stylization with Multiview Score Distillation
- 通过多视角网格分数与镜像梯度融合，增强生成一致性。
- 在360度视角下实现身份保留率提升，视觉质量显著改善。
- 适合游戏、VR中需保持用户独特面部特征的场景。
3D头像风格化可将真实面部特征转化为艺术化表现，提升游戏与虚拟现实中的用户参与度。尽管3D感知生成器已取得显著进展，多数方法仍局限于近正面视角，难以保持原始主体的独特身份，导致输出缺乏多样性和个体特征。本文利用PanoHead模型，从完整的360度视角合成图像，提出一种新颖框架，采用负对数似然蒸馏（LD）提升身份保留能力并优化风格化质量。通过在3D GAN架构中集成多视角网格分数与镜像梯度，并引入分数排序加权技术，本方法在定性和定量层面均取得显著提升。研究不仅推动了3D头像风格化的前沿，也为扩散模型与GAN之间有效蒸馏过程提供了关键见解，尤其聚焦于身份保留这一核心挑战。
原文摘要 · Abstract (English)
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.
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