New RacketVision Benchmark Offers Unified Ball and Racket Analysis for Multiple Sports

Global: New RacketVision Benchmark Offers Unified Ball and Racket Analysis for Multiple Sports

A new computer‑vision benchmark focusing on racket sports has been released, targeting researchers in computer vision, artificial intelligence, and multimedia. The dataset, named RacketVision, was introduced by Linfeng Dong, Yuchen Yang, Hao Wu, Wei Wang, Yuenan Hou, Zhihang Zhong, and Xiao Sun in a paper submitted to arXiv on 21 Nov 2025 and revised on 28 Jan 2026. It encompasses table tennis, tennis, and badminton, providing large‑scale, fine‑grained annotations for both ball positions and racket pose.

Dataset Overview

According to the authors, RacketVision is the first publicly available resource that pairs detailed racket‑pose data with traditional ball‑tracking labels across three distinct racket sports. The annotations are designed to capture complex human‑object interactions, enabling more nuanced analysis of player technique and equipment dynamics.

Benchmark Tasks and Findings

The benchmark defines three interconnected tasks: fine‑grained ball tracking, articulated racket pose estimation, and predictive ball‑trajectory forecasting. In their evaluation, the authors report that simple concatenation of racket‑pose features with ball data degrades performance, whereas a Cross‑Attention mechanism substantially improves trajectory prediction, surpassing strong unimodal baselines.

Implications for Future Research

Researchers anticipate that the dataset will serve as a versatile platform for advancing dynamic object tracking, conditional motion forecasting, and multimodal analysis in sports contexts. By offering unified annotations, the benchmark encourages exploration of how racket‑pose information can be leveraged alongside ball dynamics.

Availability and Access

The authors have made the dataset, code, and benchmark specifications publicly accessible through a project website linked in the paper. The repository includes documentation, baseline implementations, and instructions for reproducing the reported results.

Publication Details

The work is classified under the arXiv subjects Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), and Multimedia (cs.MM). It is cited as arXiv:2511.17045 [v3] and carries the DOI https://doi.org/10.48550/arXiv.2511.17045.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.