StyleFusion360: View-Consistent Head Stylization via Adaptive Style Modulation

3D head stylization enables expressive reimagining of hu-man faces for creative visual experiences in digital media. Existing 3D-aware methods often require computationally intensive optimization orper-style fine-tuning, limiting flexibility and user control. To overcomethese challenges, we introduce StyleFusion360, a diffusion-based frame-work for multi-view consistent, identity-preserving 3D head stylizationfrom a single style reference image, without per-style training. Our ap-proach enhances the Style Fusion Attention mechanism with a style-conditioned key modulation mechanism that aligns content and stylerepresentations for fine-grained and controllable stylization. We furtherprovide a user-controllable slider for adjusting stylization intensity. Inaddition, StyleFusion360 supports local multi-edit stylization, enablingtargeted edits such as modifying hair or eyes independently. Extensiveexperiments on FFHQ and RenderMe360 demonstrate that StyleFu-sion360 produces high-quality, controllable, and visually compelling styl-izations, outperforming state-of-the-art GAN- and diffusion-based meth-ods across diverse style domains. Code is available at: https://github.com/furkanguzelant/StyleFusion360