ROAM: a Rich Object Appearance Model with Application to Rotoscoping
This addresses the painstaking task of rotoscoping for professional post-production artists by enhancing interactive control and efficiency.
The paper tackles the problem of rotoscoping in video by proposing a rich object appearance model that combines local and global appearance features with foreground landmarks, enabling efficient optimization and adaptation. The framework demonstrates qualitative and quantitative improvements over existing curve-based and pixel-wise segmentation tools.
Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-wise segmentation techniques can help for this task, professional rotoscoping tools rely on parametric curves that offer the artists a much better interactive control on the definition, editing and manipulation of the segments of interest. Sticking to this prevalent rotoscoping paradigm, we propose a novel framework to capture and track the visual aspect of an arbitrary object in a scene, given a first closed outline of this object. This model combines a collection of local foreground/background appearance models spread along the outline, a global appearance model of the enclosed object and a set of distinctive foreground landmarks. The structure of this rich appearance model allows simple initialization, efficient iterative optimization with exact minimization at each step, and on-line adaptation in videos. We demonstrate qualitatively and quantitatively the merit of this framework through comparisons with tools based on either dynamic segmentation with a closed curve or pixel-wise binary labelling.