Neural Head Avatars from Monocular RGB Videos

• P. Grassal¹*
• M. Prinzler¹*
• T. Leistner¹
• C. Rother¹
• M. Nießner²
• J. Thies³

• Heidelberg University¹
• TU Munich²
• MPI for Intelligent Systems³

• *equal contribution

This paper was published at CVPR 2022.

Abstract

We present Neural Head Avatars, a novel neural representation that explicitly models the surface geometry and appearance of an animatable human avatar that can be used for teleconferencing in AR/VR or other applications in the movie or games industry that rely on a digital human. Our representation can be learned from a monocular RGB portrait video that features a range of different expressions and views. Specifically, we propose a hybrid representation consisting of a morphable model for the coarse shape and expressions of the face, and two feed-forward networks, predicting vertex offsets of the underlying mesh as well as a view- and expression-dependent texture. We demonstrate that this representation is able to accurately extrapolate to unseen poses and view points, and generates natural expressions while providing sharp texture details. Compared to previous works on head avatars, our method provides a disentangled shape and appearance model of the complete human head (including hair) that is compatible with the standard graphics pipeline. Moreover, it quantitatively and qualitatively outperforms current state of the art in terms of reconstruction quality and novel-view synthesis.

Video

Project Page and Code

Visit our official project page. Download or fork our implementation on GitHub.

Citation

@inproceedings{grassal2022neural,
    title={Neural head avatars from monocular RGB videos},
    author={Grassal, Philip-William and Prinzler, Malte and Leistner, Titus and Rother, Carsten and Nie{\ss}ner, Matthias and Thies, Justus},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages={18653--18664},
    year={2022}
}