3D Gaussian Parametric Head Model
<|reference_start|>3D Gaussian Parametric Head Model: 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.<|reference_end|>
@article{xu2024gphm:,
    title={GPHM: Gaussian Parametric Head Model for Monocular Head Avatar
  Reconstruction},
    author={Yuelang Xu, Zhaoqi Su, Qingyao Wu, Yebin Liu},
    journal={arXiv preprint arXiv:2407.15070},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15070},
    primaryClass={cs.CV}
}