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Image-based Authoring of Herd Animations

Abstract : Animating herds of animals while achieving both convincing global shapes and plausible distributions within the herd is difficult using simulation methods. In this work, we allow users to rely on photos of real herds, which are widely available, for key-framing their animation. More precisely, we learn global and local distribution features in each photo of the input set (which may depict different numbers of animals) and transfer them to the group of animals to be animated, thanks to a new statistical learning method enabling to analyse distributions of ellipses, as well as their density and orientation fields. The animated herd reconstructs the desired distribution at each key-frame while avoiding obstacles. As our results show, our method offers both high level user control and help towards realism, enabling to easily author herd animations.
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Contributor : Pierre Ecormier-Nocca <>
Submitted on : Friday, May 17, 2019 - 11:00:09 AM
Last modification on : Wednesday, June 24, 2020 - 4:19:56 PM



Pierre Ecormier-Nocca, Julien Pettré, Pooran Memari, Marie-Paule Cani. Image-based Authoring of Herd Animations. Computer Animation and Virtual Worlds, Wiley, In press, 30 (3-4), pp.1-11. ⟨10.1002/cav.1903⟩. ⟨hal-02127824⟩



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