用生成先验增强3D高斯点云，让虚拟头像更逼真地表达表情。
EAvatar: Expression-Aware Head Avatar Reconstruction with Generative Geometry Priors
- 通过少量关键高斯点控制邻近点变形，精准捕捉面部细微表情变化。
- 结合预训练生成模型的高质量3D先验，提升重建形状准确性和收敛稳定性。
- 适合需要高保真表情驱动的AR/VR、游戏和数字人场景。
高保真头部虚拟形象在AR/VR、游戏和多媒体内容创作中至关重要。近期基于3D高斯溅射（3DGS）的方法在实时渲染下实现了复杂几何建模，已被广泛应用于头部重建任务。然而，现有3DGS方法在捕捉精细面部表情和保持局部纹理连续性方面仍存在挑战，尤其在高度可变形区域表现不佳。为此，本文提出一种新型3DGS框架EAvatar，具备表情感知与形变感知能力。该方法引入稀疏表情控制机制，仅用少量关键高斯点影响其邻近点的形变，从而实现局部形变与细粒度纹理过渡的精确建模。同时，利用预训练生成模型提供的高质量3D先验，为面部结构提供可靠指导，显著提升训练过程中的收敛稳定性和形状准确性。实验表明，本方法生成的头部重建结果更具准确性与视觉一致性，表达可控性与细节保真度均获提升。
原文摘要 · Abstract (English)
High-fidelity head avatar reconstruction plays a crucial role in AR/VR, gaming, and multimedia content creation. Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated effectiveness in modeling complex geometry with real-time rendering capability and are now widely used in high-fidelity head avatar reconstruction tasks. However, existing 3DGS-based methods still face significant challenges in capturing fine-grained facial expressions and preserving local texture continuity, especially in highly deformable regions. To mitigate these limitations, we propose a novel 3DGS-based framework termed EAvatar for head reconstruction that is both expression-aware and deformation-aware. Our method introduces a sparse expression control mechanism, where a small number of key Gaussians are used to influence the deformation of their neighboring Gaussians, enabling accurate modeling of local deformations and fine-scale texture transitions. Furthermore, we leverage high-quality 3D priors from pretrained generative models to provide a more reliable facial geometry, offering structural guidance that improves convergence stability and shape accuracy during training. Experimental results demonstrate that our method produces more accurate and visually coherent head reconstructions with improved expression controllability and detail fidelity.
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