Monocular 3D Reconstruction of Cheetahs in the Wild

This paper introduces a framework for monocular 3D reconstruction of cheetah movements, leveraging a combi- nation of data-driven and physics-based modeling as well as trajectory optimization. Unlike traditional methods that rely solely on kinematics, our approach integrates dynamic motion principles, enhancing the plausibility and generalization of motion estimates. Validated on the cheetah running dataset, AcinoSet, we achieve mean per-joint position errors of 78.8 mm and 72.5 mm, showcasing significant advancements over the existing model used in AcinoSet. By addressing the challenge of absent ground truth data, this work not only advances animal motion capture techniques but also informs the development of bio-inspired robotic systems, offering a robust solution for accurately capturing complex animal locomotion in natural settings.