  Existing correspondence datasets for two-dimensional (2D) cartoon suffer from
simple frame composition and monotonic movements, making them insufficient to
simulate real animations. In this work, we present a new 2D animation visual
correspondence dataset, AnimeRun, by converting open source three-dimensional
(3D) movies to full scenes in 2D style, including simultaneous moving
background and interactions of multiple subjects. Our analyses show that the
proposed dataset not only resembles real anime more in image composition, but
also possesses richer and more complex motion patterns compared to existing
datasets. With this dataset, we establish a comprehensive benchmark by
evaluating several existing optical flow and segment matching methods, and
analyze shortcomings of these methods on animation data. Data, code and other
supplementary materials are available at
https://lisiyao21.github.io/projects/AnimeRun.

Computer Vision and Pattern Recognition