Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation.
针对长发个体生成逼真的说话人脸视频，并捕捉精细面部特征是现有方法中的一大挑战。为了解决这些问题，我们提出了一种基于三维高斯散射（3D Gaussian Splatting, 3DGS）的说话人脸合成方法，称为分解式每嵌入高斯场（DEGSTalk），用于生成逼真的长发说话人脸。 我们的DEGSTalk采用可变形预嵌入高斯场（Deformable Pre-Embedding Gaussian Fields），通过隐式表情系数动态调整预嵌入的高斯原语，从而实现对动态面部区域和细微表情的精准捕捉。此外，我们提出了一种动态头发保留人像渲染技术（Dynamic Hair-Preserving Portrait Rendering），以增强合成视频中长发运动的真实感。 实验结果表明，DEGSTalk在合成质量和真实感方面相比现有方法取得了显著提升，尤其是在处理复杂的面部动态和头发保留方面表现优异。