  We propose a solution to the novel task of rendering sharp videos from new
viewpoints from a single motion-blurred image of a face. Our method handles the
complexity of face blur by implicitly learning the geometry and motion of faces
through the joint training on three large datasets: FFHQ and 300VW, which are
publicly available, and a new Bern Multi-View Face Dataset (BMFD) that we
built. The first two datasets provide a large variety of faces and allow our
model to generalize better. BMFD instead allows us to introduce multi-view
constraints, which are crucial to synthesizing sharp videos from a new camera
view. It consists of high frame rate synchronized videos from multiple views of
several subjects displaying a wide range of facial expressions. We use the high
frame rate videos to simulate realistic motion blur through averaging. Thanks
to this dataset, we train a neural network to reconstruct a 3D video
representation from a single image and the corresponding face gaze. We then
provide a camera viewpoint relative to the estimated gaze and the blurry image
as input to an encoder-decoder network to generate a video of sharp frames with
a novel camera viewpoint. We demonstrate our approach on test subjects of our
multi-view dataset and VIDTIMIT.
