Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction

2021

Conference Paper

ncs

We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. To handle the dynamics of the face, we combine our scene representation network with a low-dimensional morphable model which provides explicit control over pose and expressions. We use volumetric rendering to generate images from this hybrid representation and demonstrate that such a dynamic neural scene representation can be learned from monocular input data only, without the need of a specialized capture setup.

@inproceedings{gafni2021nerface,
  title = {Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction},
  author = {Gafni, Guy and Thies, Justus and Zoll{\"o}fer, Michael and Nie{\ss}ner, Matthias},
  booktitle = {IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
  doi = {},
  url = {https://justusthies.github.io/posts/nerface/}
}