单图生成多视角一致的人脸风格化，无需逐风格训练。
StyleFusion360: View-Consistent Head Stylization via Adaptive Style Modulation
- 用自适应风格调制实现内容与风格对齐，支持精细控制。
- 可调节风格强度滑块，支持局部编辑如换发型或眼睛。
- 在FFHQ和RenderMe360上超越现有生成模型效果。
3D人脸风格化为数字媒体中的创意视觉体验提供了表达性重构人类面部的能力。现有3D感知方法通常需要计算量大的优化或针对每种风格的微调，限制了灵活性和用户控制。为克服这些挑战，我们提出StyleFusion360，一种基于扩散模型的框架，仅需单张风格参考图即可实现多视角一致、身份保留的3D人脸风格化，且无需针对每种风格进行训练。我们的方法通过引入风格条件键调制机制，增强风格融合注意力机制，使内容与风格表征对齐，实现细粒度可控的风格化。此外，提供用户可调节的滑块以控制风格强度。同时，StyleFusion360支持局部多编辑风格化，可独立修改发色或眼部特征等。在FFHQ和RenderMe360上的大量实验表明，该方法生成高质量、可控且视觉吸引人的风格化结果，在多种风格领域中优于最先进的GAN与扩散模型方法。
原文摘要 · Abstract (English)
3D head stylization enables expressive reimagining of human faces for creative visual experiences in digital media. Existing 3D-aware methods often require computationally intensive optimization or per-style fine-tuning, limiting flexibility and user control. To overcome these challenges, we introduce StyleFusion360, a diffusion-based framework for multi-view consistent, identity-preserving 3D head stylization from a single style reference image, without per-style training. Our approach enhances the Style Fusion Attention mechanism with a style-conditioned key modulation mechanism that aligns content and style representations for fine-grained and controllable stylization. We further provide a user-controllable slider for adjusting stylization intensity. In addition, StyleFusion360 supports local multi-edit stylization, enabling targeted edits such as modifying hair or eyes independently. Extensive experiments on FFHQ and RenderMe360 demonstrate that StyleFusion360 produces high-quality, controllable, and visually compelling stylizations, outperforming state-of-the-art GAN- and diffusion-based methods across diverse style domains.
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