Characters in traditional artwork such as children's books or cartoon animations are typically drawn once, in fixed poses, with little opportunity to change the characters' appearance or re-use them in a different animation. To enable these applications one can fit a consistent parametric deformable model - a puppet - to different images of a character, thus establishing consistent segmentation, dense semantic correspondence, and deformation parameters across poses. In this work, we argue that a layered deformable puppet is a natural representation for hand-drawn characters, providing an effective way to deal with the articulation, expressive deformation, and occlusion that are common to this style of artwork. Our main contribution is an automatic pipeline for fitting these models to unlabeled images depicting the same character in various poses. We demonstrate that the output of our pipeline can be used directly for editing and re-targeting animations.

@inproceedings{Fan:2018:TAL,
   author = "Xinyi Fan and Amit H. Bermano and Vladimir G. Kim and Jovan Popovi{\'c}
      and Szymon Rusinkiewicz",
   title = "{ToonCap}: A Layered Deformable Model for Capturing Poses From Cartoon
      Characters",
   booktitle = "Expressive",
   year = "2018",
   month = aug
}