We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having the perception (pose, illumination and expression) fixed. We achieve this by a novel feed-forward encoder-decoder network architecture that is conditioned on the high-level representation of a person's facial image. The network is global, in the sense that it does not need to be retrained for a given video or for a given identity, and it creates natural looking image sequences with little distortion in time.

No abstract

Figure 1. Make-A-Scene: Samples of generated images from text inputs (a), and a text and scene input (b). Our method is able to both generate the scene (a, bottom left) and image, or generate the image from text and a simple sketch input (b, center).

No abstract