Human Pose Estimation in Trampoline Gymnastics: How to Improve Performance on Extreme Poses
Trampoline gymnastics involves extreme human poses and uncommon viewpoints, on which state-of-the art pose estimation models tend to under-perform. We demonstrate that this problem can be addressed by fine-tuning a pose estimation model on a combination of real extreme poses and domain-specific synthetic poses (STP). We generate STP from motion capture recordings of trampoline routines. We propose a pipeline to fit noisy motion capture data to a parametric human model, then generate multi-view realistic images with high-fidelity keypoint labels. The fine-tuned ViTPose model tested on real multi-view images exhibits accuracy improvements in 2D which translate to improved 3D triangulation. ...
| Authors | Léa Drolet-Roy, Victor Nogues, Bérenger Chedal-Anglay et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 17 August 2026 |
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