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

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Titel: Herds From Video: Learning a Microscopic Herd Model From Macroscopic Motion Data.
Autoren: Gong, Xianjin1 (AUTHOR) xianjin.gong@polytechnique.edu, Gain, James2 (AUTHOR) jgain@cs.uct.ac.za, Rohmer, Damien1 (AUTHOR) Damien.Rohmer@polytechnique.edu, Lyonnet, Sixtine1 (AUTHOR) sixtine.lyonnet@polytechnique.edu, Pettré, Julien3 (AUTHOR) julien.pettre@inria.fr, Cani, Marie‐Paule1 (AUTHOR) marie-paule.cani@polytechnique.edu
Quelle: Computer Graphics Forum. Sep2025, Vol. 44 Issue 6, p1-16. 16p.
Schlagwörter: *SIMULATION methods & models, ANIMAL behavior, MOTION analysis, MULTIAGENT systems, SWARMING (Zoology)
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. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
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. [ABSTRACT FROM AUTHOR]
ISSN:01677055
DOI:10.1111/cgf.70225