Neural Head Avatars from Monocular RGB Videos

Philip-William Grassal1*    Malte Prinzler1*    Titus Leistner1    Carsten Rother1    Matthias Nießner2    Justus Thies3   

1Heidelberg University    2Technical University of Munich    3Max Planck Institute for Intelligent Systems

*equal contribution

Accepted to CVPR 2022

Paper Code


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.


Paper - ArXiv - pdf (abs)



                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},