Player Disjoint Multi-Task Tennis Stroke Analysis from Monocular Pose Sequences
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The paper presents a training‑free method for detecting which holds a climber uses in sport climbing videos by leveraging a frozen foundation pose model (Sapiens) that provides fingertip and toe keypoints. Using a simple proximity test, mutual exclusion, and a temporal‑persistence rule, the approach achieves high F_1 scores (up to 90.2%) on the Way Up dataset without any climbing‑specific training, outperforming repurposed pose pipelines. The resulting automatic predictions enable accurate coaching statistics, such as climb time and pace, with Pearson correlations of 1.00 and 0.94 respectively.
arXiv:2606. 24960v1 Announce Type: new Abstract: Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed.
The paper presents a system for assessing mountain‑bike jump quality using weak binary labels and candidate feedback. It processes 95 field‑recorded jump clips with frozen V‑JEPA 2 video features, pose and motion measurements, and a transformer to predict a binary score. The model achieves an AUROC of 0.768 and average precision of 0.812 in cross‑validation, but the results highlight limitations in rider‑independent scoring and reliable feedback.
The paper introduces PART, a multimodal predictive framework for tennis that combines physiological, training, sleep, questionnaire, jump, and video data from nine collegiate players to assess overall wellness, injury risk, physical capability, and playing style. Using machine learning and deep learning, PART provides holistic athlete assessments and forecasts specific injury risks to body areas such as elbows and knees. Evaluation shows strong performance in predicting wellness and injury risk, with potential benefits for recreational players who often injure themselves due to poor technique.
arXiv:2610.32398v1 Announce Type: new Abstract: The 3rd WEAR Dataset Challenge asks for an activity label for every one-second window of four unseen participants. Each window has 15 VideoMAEv2 frame...
The paper introduces a new benchmark of 345 annotated tennis ball bounces and racket hits across eight video clips featuring diverse viewpoints, including broadcast, fixed elevated, phone, and court-level angles. It evaluates several event detection methods—supervised spotters, zero-shot vision-language models, and a parameter-free arbitration layer called RallyArb—showing that supervised models perform well on broadcast footage but poorly on non-broadcast views, while RallyArb maintains high bounce detection across all viewpoints but struggles with precise hit timing. The dataset, evaluation tools, and frozen predictions are publicly released to support further research.