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BetweenIT: An Interactive Tool for Tight Inbetweening
Author(s) -
Whited Brian,
Noris Gioacchino,
Simmons Maryann,
Sumner Robert W.,
Gross Markus,
Rossignac Jarek
Publication year - 2010
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2009.01630.x
Subject(s) - computer science , animation , key frame , artificial intelligence , computer vision , interpolation (computer graphics) , computer graphics (images) , context (archaeology) , feature (linguistics) , process (computing) , set (abstract data type) , focus (optics) , curvature , computer animation , component (thermodynamics) , vertex (graph theory) , theoretical computer science , frame (networking) , mathematics , graph , telecommunications , paleontology , linguistics , philosophy , physics , geometry , thermodynamics , optics , biology , programming language , operating system
The generation of inbetween frames that interpolate a given set of key frames is a major component in the production of a 2D feature animation. Our objective is to considerably reduce the cost of the inbetweening phase by offering an intuitive and effective interactive environment that automates inbetweening when possible while allowing the artist to guide, complement, or override the results. Tight inbetweens, which interpolate similar key frames, are particularly time‐consuming and tedious to draw. Therefore, we focus on automating these high‐precision and expensive portions of the process. We have designed a set of user‐guided semi‐automatic techniques that fit well with current practice and minimize the number of required artist‐gestures. We present a novel technique for stroke interpolation from only two keys which combines a stroke motion constructed from logarithmic spiral vertex trajectories with a stroke deformation based on curvature averaging and twisting warps. We discuss our system in the context of a feature animation production environment and evaluate our approach with real production data.