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 keyframing 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 analyze distributions of ellipses, as well as their density and orientation fields. The animated herd reconstructs the desired distribution at each keyframe while avoiding obstacles. As our results show, our method offers both high‐level user control and help toward realism, enabling to easily author herd animations.

中文翻译：

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基于图像的牛群动画创作

使用模拟方法，在实现令人信服的全局形状和牛群内合理分布的同时对动物群进行动画处理是很困难的。在这项工作中，我们允许用户依靠广泛可用的真实牛群的照片来为他们的动画设置关键帧。更准确地说，我们在输入集的每张照片（可能描绘不同数量的动物）中学习全局和局部分布特征，并将它们转移到要动画的动物组，这要归功于一种新的统计学习方法，能够分析椭圆，以及它们的密度和方向场。动画羊群在每个关键帧重建所需的分布，同时避开障碍物。正如我们的结果所示，我们的方法既提供了高级用户控制，又有助于实现真实感，从而能够轻松创作群体动画。