Creating high-fidelity 3D human head avatars is crucial for applications in VR/AR, telepresence, digital human interfaces, and film production. Recent advances have leveraged morphable face models to generate animated head avatars from easily accessible data, representing varying identities and expressions within a low-dimensional parametric space. However, existing methods often struggle with modeling complex appearance details, e.g., hairstyles and accessories, and suffer from low rendering quality and efficiency. This paper introduces a novel approach, 3D Gaussian Parametric Head Model, which employs 3D Gaussians to accurately represent the complexities of the human head, allowing precise control over both identity and expression. Additionally, it enables seamless face portrait interpolation and the reconstruction of detailed head avatars from a single image. Unlike previous methods, the Gaussian model can handle intricate details, enabling realistic representations of varying appearances and complex expressions. Furthermore, this paper presents a well-designed training framework to ensure smooth convergence, providing a guarantee for learning the rich content. Our method achieves high-quality, photo-realistic rendering with real-time efficiency, making it a valuable contribution to the field of parametric head models.
创建高保真度的3D人头头像对于虚拟现实/增强现实、远程存在、数字人界面和电影制作等应用至关重要。近期的进展利用了可变形面部模型,从易于获取的数据生成动画头像,在低维参数空间内表示不同的身份和表情。然而,现有方法往往难以模拟复杂的外观细节,如发型和配饰,并且存在渲染质量低和效率低的问题。本文介绍了一种新颖的方法,3D高斯参数化头部模型,该模型采用3D高斯分布精确表示人头的复杂性,允许对身份和表情进行精确控制。此外,它还能实现无缝的面部肖像插值和从单一图像重建详细的头像。与之前的方法不同,高斯模型可以处理复杂的细节,能够逼真地表现各种外观和复杂的表情。此外,本文提出了一个精心设计的训练框架,以确保平稳收敛,为学习丰富内容提供保证。我们的方法实现了高质量、真实感的渲染,同时具有实时效率,为参数化头部模型领域做出了宝贵贡献。