DiffStyle360: Diffusion-Based 360° Head Stylization via Style Fusion Attention

• URL: http://arxiv.org/abs/2511.22411v1
• Date: Thu, 27 Nov 2025 12:42:37 GMT
• Title: DiffStyle360: Diffusion-Based 360° Head Stylization via Style Fusion Attention
• Abstract summary: 3D head stylization has emerged as a key technique for reimagining realistic human heads in various artistic forms.<n>We propose DiffStyle360, a diffusion-based framework capable of producing consistent, identity-preserving 3D head stylizations.<n>We show that DiffStyle360 achieves superior style quality, outperforming state-of-the-art GAN- and diffusion-based stylization methods.
• Score: 12.70036301582592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D head stylization has emerged as a key technique for reimagining realistic human heads in various artistic forms, enabling expressive character design and creative visual experiences in digital media. Despite the progress in 3D-aware generation, existing 3D head stylization methods often rely on computationally expensive optimization or domain-specific fine-tuning to adapt to new styles. To address these limitations, we propose DiffStyle360, a diffusion-based framework capable of producing multi-view consistent, identity-preserving 3D head stylizations across diverse artistic domains given a single style reference image, without requiring per-style training. Building upon the 3D-aware DiffPortrait360 architecture, our approach introduces two key components: the Style Appearance Module, which disentangles style from content, and the Style Fusion Attention mechanism, which adaptively balances structure preservation and stylization fidelity in the latent space. Furthermore, we employ a 3D GAN-generated multi-view dataset for robust fine-tuning and introduce a temperaturebased key scaling strategy to control stylization intensity during inference. Extensive experiments on FFHQ and RenderMe360 demonstrate that DiffStyle360 achieves superior style quality, outperforming state-of-the-art GAN- and diffusion-based stylization methods across challenging style domains.

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