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This paper presents a neural rendering method for controllable portrait videosynthesis. Recent advances in volumetric neural rendering, such as neuralradiance fields (NeRF), has enabled the photorealistic novel view synthesis ofstatic scenes with impressive results. However, modeling dynamic andcontrollable objects as part of a scene with such scene representations isstill challenging. In this work, we design a system that enables both novelview synthesis for portrait video, including the human subject and the scenebackground, and explicit control of the facial expressions through alow-dimensional expression representation. We leverage the expression space ofa 3D morphable face model (3DMM) to represent the distribution of human facialexpressions, and use it to condition the NeRF volumetric function. Furthermore,we impose a spatial prior brought by 3DMM fitting to guide the network to learndisentangled control for scene appearance and facial actions. We demonstratethe effectiveness of our method on free view synthesis of portrait videos withexpression controls. To train a scene, our method only requires a short videoof a subject captured by a mobile device.

FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation