Learning to Synthesize and Manipulate Natural Images

Humans are avid consumers of visual content. Every day, people watch videos, play games, and share photos on social media. However, there is an asymmetry—while everybody is able to consume visual data, only a chosen few are talented enough to express themselves visually. For the rest of us, most attempts at creating realistic visual content end up quickly “falling off” what we could consider to be natural images. In this thesis, we investigate several machine learning approaches for preserving visual realism while creating and manipulating photographs. We use these methods as training wheels for visual content creation. These methods not only help users easily synthesize realistic photos but also enable previously not possible visual effects.