Animal Motion

Research on animal motion focuses on accurately capturing, analyzing, and generating realistic animal movements. Current efforts involve developing advanced motion capture techniques, such as using airship formations for improved observation of animals in their natural habitats, and leveraging machine learning models, including generative transformer networks and Koopman operator methods, to synthesize animal motion from limited data or textual descriptions. These advancements are improving our understanding of animal behavior and enabling the creation of highly realistic virtual animals for applications in entertainment, virtual reality, and scientific visualization.