Lama Séoud | Searcharxiv

arXiv2026

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. 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.

cs.CV↗

arXiv2026

Step-Attention Refinement of DINOv3 Features for Efficient Anterior Eye Segmentation

Anterior eye segment (AES) segmentation is a key component of both ocular biometrics and emerging clinical image analysis applications. However, heterogeneous acquisition conditions and limited annotations in medical settings hinder the robustness and generalization of existing methods. Foundation models (FMs) such as DINOv3 offer strong transfer capabilities, but efficiently adapting their representations to dense prediction tasks remains challenging. In this study, we investigate robust AES segmentation in clinical settings, and propose a lightweight architecture built upon a distilled DINOv3 ViT-Small backbone. We introduce a step-attention feature refinement module that progressively adapts multi-level transformer representations before convolutional decoding, enabling efficient exploitation of pretrained features with few parameters. We evaluate the proposed approach on a private dataset of 333 clinically acquired AES images spanning eight ophthalmic acquisition protocols and annotated for seven anatomical classes. Compared with convolutional and transformer-based baselines, including DINOv3-based methods, our approach achieves the best overall performance, reaching 85.55\% mIoU when fully fine-tuned. It also demonstrates the strongest robustness to domain shift across four unseen public AES segmentation datasets. These results establish a strong baseline for robust AES segmentation in clinical settings and highlight the importance of decoder design for effectively adapting FMs representations to medical segmentation tasks.

cs.CV↗