mlsanityData to 14 Oct 2026

Wild Motion Unleashed: Markerless 3D Kinematics and Force Estimation in Cheetahs

arXiv 2312.05879 ↗First posted 10 Dec 2023cs.CVcited by 0

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Abstract (arXiv)

The complex dynamics of animal manoeuvrability in the wild is extremely challenging to study. The cheetah (textitAcinonyx jubatus) is a perfect example: despite great interest in its unmatched speed and manoeuvrability, obtaining complete whole-body motion data from these animals remains an unsolved problem. This is especially difficult in wild cheetahs, where it is essential that the methods used are remote and do not constrain the animal's motion. In this work, we use data obtained from cheetahs in the wild to present a trajectory optimisation approach for estimating the 3D kinematics and joint torques of subjects remotely. We call this approach kinetic full trajectory estimation (K-FTE). We validate the method on a dataset comprising synchronised video and force plate data. We are able to reconstruct the 3D kinematics with an average reprojection error of 17.69 pixels (62.94 % PCK using the nose-to-eye(s) length segment as a threshold), while the estimates produce an average root-mean-square error of 171.3 N (≈ 17.16 % of peak force during stride) for the estimated ground reaction force when compared against the force plate data. While the joint torques cannot be directly validated against ground truth data, as no such data is available for cheetahs, the estimated torques agree with previous studies of quadrupeds in controlled settings. These results will enable deeper insight into the study of animal locomotion in a more natural environment for both biologists and roboticists.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 35 references that are in the map. First posted on arXiv 10 Dec 2023.

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9 of 35 references.

References · 9

  1. 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 494
  2. 2017A simple yet effective baseline for 3d human pose estimationcited by 401
  3. 20163D Menagerie: Modeling the 3D shape and pose of animalscited by 312
  4. 2018Markerless tracking of user-defined features with deep learningcited by 206
  5. 2020Contact and Human Dynamics from Monocular Videocited by 95
  6. 2018Creatures great and SMAL: Recovering the shape and motion of animals from videocited by 84
  7. 2019Pretraining boosts out-of-domain robustness for pose estimationcited by 47
  8. 2021AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wildcited by 30
  9. 2021Estimating 3D Motion and Forces of Human-Object Interactions from Internet Videoscited by 9

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