Modeling Fly Social Behavior with Agent-Centric Pose Forecasting
This work introduces an agent-centric framework to train models of animal behavior by forcing deep networks to mimic a specific animal's sensory inputs and motor outputs. It uses a library called AnimalPoseForecasting to manage complex data representations, allowing researchers to study how animals generate behavior rather than just fitting movement trajectories. The results show this approach captures fine-grained social behaviors accurately.