Data-driven Methods for Interactive Visual Content Creation and Manipulation

manual interaction at several stages of the process. Coupled with long processing and acquisition times, content production is rather costly and poses
a potential barrier to many applications. Although cameras now allow anyone to easily capture photos and video, tools for manipulating such media
demand both artistic talent and technical expertise. However, at the same time, vast copuses with existing visual content such as Flickr, Youtube or Google
3D Warehouse are today available and easily accessible.
 
This thesis proposes a data-driven approach to tackle the above mentioned problems encountered in content generation. To this end, statistical models
trained on semantic knowledge harvested from existing visual content corpuses are created. Using these models, we then develop tools which are easy to
learn and use even by novice users but still produce high-quality content. These tools have intuitive interfaces, and enable the user to have a precise and
flexible control. Specifically, we apply our models to create tools to simplify the tasks of video manipulation, 3D modeling and material assignment to
3D objects.