BasketballGAN: Generating basketball play simulation through sketching

Hsin Ying Hsieh, Chieh Yu Chen, Yu-Shuen Wang, Jung-Hong Chuang

研究成果: Conference contribution同行評審

摘要

We present a data-driven basketball set play simulation. Given an offensive set play sketch, our method simulates potential scenarios that may occur in the game. The simulation provides coaches and players with insights on how a given set play can be executed. To achieve the goal, we train a conditional adversarial network on NBA movement data to imitate the behaviors of how players move around the court through two major components: a generator that learns to generate natural player movements based on a latent noise and a user sketched set play; and a discriminator that is used to evaluate the realism of the basketball play. To improve the quality of simulation, we minimize 1.) a dribbler loss to prevent the ball from drifting away from the dribbler; 2.) a defender loss to prevent the dribbler from not being defended; 3.) a ball passing loss to ensure the straightness of passing trajectories; and 4) an acceleration loss to minimize unnecessary players' movements. To evaluate our system, we objectively compared real and simulated basketball set plays. Besides, a subjective test was conducted to judge whether a set play was real or generated by our network. On average, the mean correct rates to the binary tests were 56.17 %. Experiment results and the evaluations demonstrated the effectiveness of our system.

原文English
主出版物標題MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
發行者Association for Computing Machinery, Inc
頁面720-728
頁數9
ISBN(電子)9781450368896
DOIs
出版狀態Published - 15 十月 2019
事件27th ACM International Conference on Multimedia, MM 2019 - Nice, France
持續時間: 21 十月 201925 十月 2019

出版系列

名字MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia

Conference

Conference27th ACM International Conference on Multimedia, MM 2019
國家France
城市Nice
期間21/10/1925/10/19

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