Herds From Video: Learning a Microscopic Herd Model From Macroscopic Motion Data

Gong, Xianjin and Gain, James and Rohmer, Damien and Lyonnet, Sixtine and Pettre, Julien and Cani, Marie-Paule (2025) Herds From Video: Learning a Microscopic Herd Model From Macroscopic Motion Data, Computer Graphics Forum, 44.

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Abstract

We present a method for animating herds that automatically tunes a microscopic herd model based on a short video clip of real animals. Our method handles videos with dense herds, where individual animal motion cannot be separated out. Our contribution is a novel framework for extracting macroscopic herd behaviour from such video clips, and then deriving the microscopic agent parameters that best match this behaviour. To support this learning process, we extend standard agent models to provide a separation between leaders and followers, better match the occlusion and field-of-view limitations of real animals, support differentiable parameter optimization and improve authoring control. We validate the method by showing that once optimized, the social force and perception parameters of the resulting herd model are accurate enough to predict subsequent frames in the video, even for macroscopic properties not directly incorporated in the optimization process. Furthermore, the extracted herding characteristics can be applied to any terrain with a palette and region-painting approach that generalizes to different herd sizes and leader trajectories. This enables the authoring of herd animations in new environments while preserving learned behaviour.

Item Type:	Journal article (paginated)
Uncontrolled Keywords:	animation, behavioural animation
Subjects:	Computing methodologies Computing methodologies > Computer graphics > Animation > Physical simulation
Date Deposited:	20 Oct 2025 08:39
Last Modified:	20 Oct 2025 08:39
URI:	https://pubs.cs.uct.ac.za/id/eprint/1775

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