VRMM：一种体积可重光照可变形头部模型
VRMM: A Volumetric Relightable Morphable Head Model
AI总结：
本文提出体积可重光照可变形头部模型VRMM，通过自监督框架解耦身份、表情与光照，并结合保持先验的个性化方法，实现单肖像高质量3D人脸重建、重光照与动画。
摘要：
本文提出了体积可重光照可变形模型（Volumetric Relightable Morphable Model，VRMM），这是一种用于3D人脸建模的新型体积化参数化面部先验。尽管近期的体积先验模型相较3D可变形模型（3D Morphable Models，3DMMs）等传统方法有所改进，但它们在模型学习和个性化重建方面仍面临挑战。我们的VRMM通过采用一种新型训练框架克服这些问题，该框架能够高效地解耦并编码身份、表情和光照的潜在空间，将其表示为低维形式。该框架采用自监督学习设计，显著降低了对训练数据的约束，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。我们通过虚拟人生成、面部重建和动画等多种应用展示了VRMM的多功能性与有效性。此外，我们基于VRMM提出了一种新型保持先验的个性化框架，以解决生成式体积模型中常见的过拟合问题。该方法甚至能够仅凭单张肖像输入实现高质量3D人脸重建。实验表明，VRMM具有显著推动3D人脸建模领域发展的潜力。
英文摘要：
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.
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