The ability to identify objects or region correspondences between consecutive frames of a given hand-drawn animation sequence is an indispensable tool for automating animation modification tasks such as sequence-wide recoloring or shape-editing of a specific animated character. Existing correspondence identification methods heavily rely on appearance features, but these features alone are insufficient to reliably identify region correspondences when there exist occlusions or when two or more objects share similar appearances. To resolve the above problems, manual assistance is often required. In this paper, we propose a new correspondence identification method which considers both appearance features and motions of regions in a global manner. We formulate correspondence likelihoods between temporal region pairs as a network flow graph problem which can be solved by a well-established optimization algorithm. We have evaluated our method with various animation sequences and results show that our method consistently outperforms the state-of-the-art methods without any user guidance.

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@article{zhu-2016-toontrack,
    author   = {Haichao Zhu and Xueting Liu and
                Tien-Tsin Wong and Pheng-Ann Heng},
    title    = {Globally Optimal Toon Tracking},
    journal  = {ACM Transactions on Graphics
               (SIGGRAPH 2016 issue)},
    month    = {July},
    year     = {2016},
    volume   = {35},
    number   = {4},
    pages    = {75:1-75:10},
}