# Human Pose Estimation in Trampoline Gymnastics: How to Improve Performance on Extreme Poses

arXiv: https://arxiv.org/abs/2604.01322 · HTML: https://mlsanity.com/paper/2604.01322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://mlsanity.com/paper/2303.07399/) (2023)
- [3D Human Pose Estimation via Intuitive Physics](https://mlsanity.com/paper/2303.18246/) (2023)
- [Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views](https://mlsanity.com/paper/1901.04111/) (2019)
- [ViTPose++: Vision Transformer for Generic Body Pose Estimation](https://mlsanity.com/paper/2212.04246/) (2022)
- [Harvesting Multiple Views for Marker-less 3D Human Pose Annotations](https://mlsanity.com/paper/1704.04793/) (2017)
- [SOMA: Solving Optical Marker-Based MoCap Automatically](https://mlsanity.com/paper/2110.04431/) (2021)
- [BEDLAM2.0: Synthetic Humans and Cameras in Motion](https://mlsanity.com/paper/2511.14394/) (2025)
- [Generalizable Human Pose Triangulation](https://mlsanity.com/paper/2110.00280/) (2021)
- [WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Users](https://mlsanity.com/paper/2404.17063/) (2024)
- [Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data](https://mlsanity.com/paper/2307.06737/) (2023)

## Abstract (arXiv)

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. In 2D, we obtain a performance similar to state-of-the-art models on the MS COCO validation set while evaluating on significantly more challenging data, bridging the performance gap between common and extreme poses. In 3D, we reduce the MPJPE by 46.1 mm with our best model, which represents an improvement of 42.7% compared to the pretrained ViTPose model. Our code and data are available at https://github.com/VisionICLab/trampoline_syn_data.