# ROAM: a Rich Object Appearance Model with Application to Rotoscoping

arXiv: https://arxiv.org/abs/1612.01495 · HTML: https://mlsanity.com/paper/1612.01495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [SiamMask: A Framework for Fast Online Object Tracking and Segmentation](https://mlsanity.com/paper/2207.02088/) (2022)
- [Polygonal Point Set Tracking](https://mlsanity.com/paper/2105.14584/) (2021)
- [Color-aware Deep Temporal Backdrop Duplex Matting System](https://mlsanity.com/paper/2306.02954/) (2023)

## Abstract (arXiv)

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.