ROAM: A Rich Object Appearance Model With Application to Rotoscoping

Ondrej Miksik, Juan-Manuel Perez-Rua, Philip H. S. Torr, Patrick Perez; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4691-4699

Abstract


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.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Miksik_2017_CVPR,
author = {Miksik, Ondrej and Perez-Rua, Juan-Manuel and Torr, Philip H. S. and Perez, Patrick},
title = {ROAM: A Rich Object Appearance Model With Application to Rotoscoping},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}