ROAM: a Rich Object Appearance Model with Application to Rotoscoping

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 seg-mentation with a closed curve or pixel-wise binary labelling.
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Contributor : Juan-Manuel Perez-Rua <>
Submitted on : Tuesday, August 22, 2017 - 5:37:09 PM
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  • HAL Id : hal-01576017, version 1



Ondrej Miksik, Juan-Manuel Pérez-Rúa, Philip Torr, Patrick Pérez. ROAM: a Rich Object Appearance Model with Application to Rotoscoping. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul 2017, Honolulu, United States. ⟨hal-01576017⟩



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