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Keyframe-based tracking for rotoscoping and animation

Authors:

Aseem Agarwala,

Aaron Hertzmann,

David H. Salesin,

Steven M. SeitzAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 23, Issue 3

Pages 584 - 591

https://doi.org/10.1145/1015706.1015764

Published: 01 August 2004 Publication History

Abstract

We describe a new approach to rotoscoping --- the process of tracking contours in a video sequence --- that combines computer vision with user interaction. In order to track contours in video, the user specifies curves in two or more frames; these curves are used as keyframes by a computer-vision-based tracking algorithm. The user may interactively refine the curves and then restart the tracking algorithm. Combining computer vision with user interaction allows our system to track any sequence with significantly less effort than interpolation-based systems --- and with better reliability than "pure" computer vision systems. Our tracking algorithm is cast as a spacetime optimization problem that solves for time-varying curve shapes based on an input video sequence and user-specified constraints. We demonstrate our system with several rotoscoped examples. Additionally, we show how these rotoscoped contours can be used to help create cartoon animation by attaching user-drawn strokes to the tracked contours.

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Cited By

Rai GGupta SSharma O(2024)SketchAnim: Real‐time sketch animation transfer from videosComputer Graphics Forum10.1111/cgf.15176Online publication date: 9-Oct-2024
https://doi.org/10.1111/cgf.15176
Zheng YCun XXia MPun C(2024)Sketch Video SynthesisComputer Graphics Forum10.1111/cgf.1504443:2Online publication date: 30-Apr-2024
https://doi.org/10.1111/cgf.15044
Du QDuan YXie ZTao XShi LJin Z(2024)Optical Flow-Based Spatiotemporal Sketch for Video Representation: A Novel FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.334913034:8(6963-6977)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3349130
Show More Cited By

Index Terms

Keyframe-based tracking for rotoscoping and animation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
  2. Computer graphics
    1. Graphics systems and interfaces
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Graphics input devices

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Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 23, Issue 3

August 2004

684 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1015706

Editor:
John C. Hart

Issue’s Table of Contents

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2004

Published in TOG Volume 23, Issue 3

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Cited By

Rai GGupta SSharma O(2024)SketchAnim: Real‐time sketch animation transfer from videosComputer Graphics Forum10.1111/cgf.15176Online publication date: 9-Oct-2024
https://doi.org/10.1111/cgf.15176
Zheng YCun XXia MPun C(2024)Sketch Video SynthesisComputer Graphics Forum10.1111/cgf.1504443:2Online publication date: 30-Apr-2024
https://doi.org/10.1111/cgf.15044
Du QDuan YXie ZTao XShi LJin Z(2024)Optical Flow-Based Spatiotemporal Sketch for Video Representation: A Novel FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.334913034:8(6963-6977)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3349130
Chen JLi QZhang WWang BZhang D(2024)A Degradation-Robust Keyframe Selection Method Based on Image Quality Evaluation for Visual LocalizationIEEE Internet of Things Journal10.1109/JIOT.2024.336579411:10(18421-18434)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3365794
Gal RVinker YAlaluf YBermano ACohen-Or DShamir AChechik G(2024)Breathing Life Into Sketches Using Text-to-Video Priors2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00414(4325-4336)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00414
Ingle PKim Y(2023)Video Synopsis Algorithms and Framework: A Survey and Comparative EvaluationSystems10.3390/systems1102010811:2(108)Online publication date: 17-Feb-2023
https://doi.org/10.3390/systems11020108
Fan JZhang KZhao YLiu Q(2023)Unsupervised Video Object Segmentation via Weak User Interaction and Temporal ModulationChinese Journal of Electronics10.23919/cje.2022.00.13932:3(507-518)Online publication date: May-2023
https://doi.org/10.23919/cje.2022.00.139
Hachmann HRosenhahn BZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)Color-aware Deep Temporal Backdrop Duplex Matting SystemProceedings of the 14th Conference on ACM Multimedia Systems10.1145/3587819.3590973(205-216)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3587819.3590973
Pan YZhang RCheng STan SDing YMitchell KYang X(2023)Emotional Voice PuppetryIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.324710129:5(2527-2535)Online publication date: 22-Feb-2023
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Zhou TPorikli FCrandall DVan Gool LWang W(2023)A Survey on Deep Learning Technique for Video SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.322557345:6(7099-7122)Online publication date: 1-Jun-2023
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