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<p>3D face stylization remains challenging due to limited training samples, diverse style domains, and the complex mapping between ambiguous style features and 3D face structures. To address these issues, we propose ClipStyleFace, a text-driven approach for 3D face stylization that leverages CLIP (Contrastive Language-Image Pre-training) knowledge to create style variations in both geometric and texture structures. ClipStyleFace comprises three components. For geometry deformation, a deformable surface is designed to model stylized geometric residuals on the initial mesh. For texture transformation, we construct a compact parameter space enabling style transfer using a pre-trained albedo generator. Both modules are optimized consistently by distilling semantic alignment and domain correction knowledge from the CLIP model. Extensive experiments demonstrate the effectiveness of our approach in generating stylized 3D faces that match target style prompts while preserving identity characteristics and facial details. Our model also holds promise for applications such as animation and image-driven 3D stylized face generation. Our code is released on <a href="https://github.com/cutegao715/ClipStyleFace">https://github.com/cutegao715/ClipStyleFace</a>.</p>

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Disentangled text-driven stylization of 3D faces via directional CLIP losses

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Wenjing Gao,
  • Xi Li,
  • Chang Liu,
  • JiaoJiao Wang,
  • Dingguo Yu

摘要

3D face stylization remains challenging due to limited training samples, diverse style domains, and the complex mapping between ambiguous style features and 3D face structures. To address these issues, we propose ClipStyleFace, a text-driven approach for 3D face stylization that leverages CLIP (Contrastive Language-Image Pre-training) knowledge to create style variations in both geometric and texture structures. ClipStyleFace comprises three components. For geometry deformation, a deformable surface is designed to model stylized geometric residuals on the initial mesh. For texture transformation, we construct a compact parameter space enabling style transfer using a pre-trained albedo generator. Both modules are optimized consistently by distilling semantic alignment and domain correction knowledge from the CLIP model. Extensive experiments demonstrate the effectiveness of our approach in generating stylized 3D faces that match target style prompts while preserving identity characteristics and facial details. Our model also holds promise for applications such as animation and image-driven 3D stylized face generation. Our code is released on https://github.com/cutegao715/ClipStyleFace.