Existing 3D face modeling methods usually depend on 3D Morphable Models, which inherently constrain the representation capacity to fixed shape priors. Optimization-based approaches offer high-quality reconstructions but tend to be computationally expensive. In this work, we introduce GLVD, a hybrid method for 3D face reconstruction from few-shot images that extends Learned Vertex Descent (LVD) by integrating per-vertex neural field optimization with global structural guidance from dynamically predicted 3D keypoints. By incorporating relative spatial encoding, GLVD iteratively refines mesh vertices without requiring dense 3D supervision. This enables expressive and adaptable geometry reconstruction while maintaining computational efficiency. GLVD achieves state-of-the-art performance in single-view settings and remains highly competitive in multi-view scenarios, all while substantially reducing inference time.

翻译：现有的三维人脸建模方法通常依赖于三维形变模型，这些模型本质上将表示能力限制在固定的形状先验中。基于优化的方法能够提供高质量的重建结果，但往往计算成本高昂。在本研究中，我们提出了GLVD，一种基于少样本图像的三维人脸重建混合方法，该方法通过学习顶点下降法的扩展，将逐顶点神经场优化与动态预测的三维关键点提供的全局结构引导相结合。通过引入相对空间编码，GLVD能够在无需密集三维监督的情况下迭代优化网格顶点。这使得模型能够实现表现力强且适应性强的几何重建，同时保持计算效率。GLVD在单视图设置中达到了最先进的性能，在多视图场景中仍保持高度竞争力，同时显著减少了推理时间。