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Learning to Deblur and Rotate Motion-Blurred Faces by Meishvili, Givi; Szabó, Attila; Jenni, Simon; Favaro, Paolo is a scholarly article available to read on EtoBox.

What is Learning to Deblur and Rotate Motion-Blurred Faces about?

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

Author

Meishvili, Givi; Szabó, Attila; Jenni, Simon; Favaro, Paolo

Published

2021

Language

EN