標題: BasketballGAN: Generating Basketball Play Simulation Through Sketching
作者: Hsieh, Hsin-Ying
Chen, Chieh-Yu
Wang, Yu-Shuen
Chuang, Jung-Hong
交大名義發表
National Chiao Tung University
關鍵字: Conditional adversarial network;basketball;Sketch;Simulation
公開日期: 1-一月-2019
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.
URI: http://dx.doi.org/10.1145/3343031.3351050
http://hdl.handle.net/11536/153832
ISBN: 978-1-4503-6889-6
DOI: 10.1145/3343031.3351050
期刊: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19)
起始頁: 720
結束頁: 728
顯示於類別:會議論文