AI-Cafe presents: Image and Video Generation. A deep Learning Approach

In this talk Prof. Sebe will present some of our recent achievements addressing these specific aspects:

1. generating facial expressions, e.g., smiles that are different from each other (e.g., spontaneous, tense, etc.) using diversity as the driving force.
2. generating videos without using any annotation or prior information about the specific object to animate. Once trained on a set of videos depicting objects of the same category (e.g. faces, human bodies), our method can be applied to any object of this class.

To achieve this, we decouple appearance and motion information using a self-supervised formulation. To support complex motions, we use a representation consisting of a set of learned key points along with their local affine transformations. A generator network models occlusions arising during target motions and combines the appearance extracted from the source image and the motion derived from the driving video. Our solutions score best on diverse benchmarks and on a variety of object categories.