3D Gaussian representations have emerged as a powerful paradigm for digital head modeling, achieving photorealistic quality with real-time rendering. However, intuitive and interactive creation or editing of 3D Gaussian head models remains challenging. Although 2D sketches provide an ideal interaction modality for fast, intuitive conceptual design, they are sparse, depth-ambiguous, and lack high-frequency appearance cues, making it difficult to infer dense, geometrically consistent 3D Gaussian structures from strokes - especially under real-time constraints. To address these challenges, we propose SketchFaceGS, the first sketch-driven framework for real-time generation and editing of photorealistic 3D Gaussian head models from 2D sketches. Our method uses a feed-forward, coarse-to-fine architecture. A Transformer-based UV feature-prediction module first reconstructs a coarse but geometrically consistent UV feature map from the input sketch, and then a 3D UV feature enhancement module refines it with high-frequency, photorealistic detail to produce a high-fidelity 3D head. For editing, we introduce a UV Mask Fusion technique combined with a layer-by-layer feature-fusion strategy, enabling precise, real-time, free-viewpoint modifications. Extensive experiments show that SketchFaceGS outperforms existing methods in both generation fidelity and editing flexibility, producing high-quality, editable 3D heads from sketches in a single forward pass.

翻译：三维高斯表示已成为数字头部建模的强大范式，能以实时渲染实现照片级真实感质量。然而，三维高斯头部模型的直观交互式创建或编辑仍具挑战性。尽管二维草图为快速直观的概念设计提供了理想的交互方式，但其稀疏性、深度模糊性及缺乏高频外观线索的特性，使得从笔画中推断密集且几何一致的三维高斯结构——尤其受实时性约束——极为困难。为解决上述挑战，我们提出SketchFaceGS，这是首个从二维草图实时生成与编辑照片级真实感三维高斯头部模型的草图驱动框架。本方法采用前馈式从粗到精的架构：基于Transformer的UV特征预测模块首先从输入草图重建出粗糙但几何一致的UV特征图，随后三维UV特征增强模块以高频照片级细节对其进行细化，生成高保真三维头部。在编辑方面，我们提出UV掩膜融合技术，并结合逐层特征融合策略，实现精确、实时、自由视角的修改。大量实验表明，SketchFaceGS在生成保真度与编辑灵活性上均优于现有方法，能从单次前向传播中由草图生成高质量、可编辑的三维头部。