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Facial 3D Morphable Models are a main computer vision subject with countlessapplications and have been highly optimized in the last two decades. Thetremendous improvements of deep generative networks have created variouspossibilities for improving such models and have attracted wide interest.Moreover, the recent advances in neural radiance fields, are revolutionisingnovel-view synthesis of known scenes. In this work, we present a facial 3DMorphable Model, which exploits both of the above, and can accurately model asubject's identity, pose and expression and render it in arbitraryillumination. This is achieved by utilizing a powerful deep style-basedgenerator to overcome two main weaknesses of neural radiance fields, theirrigidity and rendering speed. We introduce a style-based generative networkthat synthesizes in one pass all and only the required rendering samples of aneural radiance field. We create a vast labelled synthetic dataset of facialrenders, and train the network on these data, so that it can accurately modeland generalize on facial identity, pose and appearance. Finally, we show thatthis model can accurately be fit to "in-the-wild" facial images of arbitrarypose and illumination, extract the facial characteristics, and be used tore-render the face in controllable conditions.

3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling