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 accurate 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：本文提出体素可重光照形变模型（VRMM），一种用于三维人脸建模的新型体素化参数化面部先验。尽管近年来的体素先验模型相比传统三维形变模型（3DMM）有所改进，但在模型学习与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有效克服这些困难，该框架能够将身份、表情和光照的隐空间高效解耦并编码为低维表征。这种基于自监督学习设计的框架大幅降低了训练数据的约束条件，使其在实践中更具可行性。学习得到的VRMM具备重光照能力，并涵盖全面的表情范围。我们通过数字人生成、人脸重建与动画等多种应用场景展示了VRMM的通用性与有效性。此外，我们基于VRMM提出了一种新颖的先验保持个性化框架，解决了生成式体素模型中普遍存在的过拟合问题。该方法即使仅通过单张肖像输入也能实现精准的三维人脸重建。实验结果表明，VRMM具有显著推动三维人脸建模领域发展的潜力。