In this paper, we introduce the Volumetric Relightable Morphable Model (VRMM), a novel volumetric and parametric facial prior for 3D face modeling. While recent volumetric prior models offer improvements over traditional methods like 3D Morphable Models (3DMMs), they face challenges in model learning and personalized reconstructions. Our VRMM overcomes these by employing a novel training framework that efficiently disentangles and encodes latent spaces of identity, expression, and lighting into low-dimensional representations. This framework, designed with self-supervised learning, significantly reduces the constraints for training data, making it more feasible in practice. The learned VRMM offers relighting capabilities and encompasses a comprehensive range of expressions. We demonstrate the versatility and effectiveness of VRMM through various applications like avatar generation, facial reconstruction, and animation. Additionally, we address the common issue of overfitting in generative volumetric models with a novel prior-preserving personalization framework based on VRMM. Such an approach enables high-quality 3D face reconstruction from even a single portrait input. Our experiments showcase the potential of VRMM to significantly enhance the field of 3D face modeling.

翻译：本文提出可重光照体素变形模型（VRMM），这是一种用于三维人脸建模的新型体素参数化面部先验。尽管近年来体素先验模型相比传统方法（如三维可变形模型3DMM）有所改进，但在模型学习和个性化重建方面仍面临挑战。我们的VRMM通过采用新型训练框架克服了这些困难，该框架能够有效解耦并编码身份、表情和光照的潜空间为低维表示。这一基于自监督学习的框架显著降低了对训练数据的约束要求，使其在实际应用中更具可行性。学习得到的VRMM具备重光照能力，并涵盖丰富的表情范围。我们通过角色生成、面部重建和动画等多种应用验证了VRMM的通用性和有效性。此外，针对生成式体素模型中常见的过拟合问题，我们提出了一种基于VRMM的新型先验保持个性化框架。该方法即使仅基于单张肖像输入，也能实现高质量的三维人脸重建。实验结果表明，VRMM有潜力显著推动三维人脸建模领域的发展。