research-article

BasketballGAN: Generating Basketball Play Simulation Through Sketching

Authors:

Hsin-Ying Hsieh,

Jung-Hong ChuangAuthors Info & Claims

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

Pages 720 - 728

https://doi.org/10.1145/3343031.3351050

Published: 15 October 2019 Publication History

Abstract

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.

Supplementary Material

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The auxiliary file includes a video for system demonstration, a video for results comparison, a set of videos that were used for user study, and a text file that specifies the answer.

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Cited By

Liu ZXie XHe MZhao WWu YCheng LZhang HWu Y(2024)Smartboard: Visual Exploration of Team Tactics with LLM AgentIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345620031:1(23-33)Online publication date: 10-Sep-2024
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Takagi SNakada Y(2024)Enhancing Formation Analysis Using Multi-layer Graph Convolutional Neural Networks and Geometric Formation Features2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)10.1109/SCISISIS61014.2024.10760047(1-6)Online publication date: 9-Nov-2024
https://doi.org/10.1109/SCISISIS61014.2024.10760047
Dash AYe JWang G(2024)A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote SensingIEEE Access10.1109/ACCESS.2023.334627312(18330-18357)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3346273
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Index Terms

BasketballGAN: Generating Basketball Play Simulation Through Sketching
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
2. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia content creation

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cover image ACM Conferences

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

October 2019

2794 pages

ISBN:9781450368896

DOI:10.1145/3343031

General Chairs:
Laurent Amsaleg
CNRS-IRISA, France
,
Benoit Huet
EURECOM, France
,
Martha Larson
Radboud University and TU Delft (Netherlands)
,
Program Chairs:
Guillaume Gravier
CNRS-IRISA, France
,
Hayley Hung
Delft University of Technology Netherlands
,
Chong-Wah Ngo
City University of Hong Kong Hong Kong
,
Wei Tsang Ooi
National University of Singapore Singapore

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Publication History

Published: 15 October 2019

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Ministry of Science and Technology, Taiwan

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MM '19

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MM '19: The 27th ACM International Conference on Multimedia

October 21 - 25, 2019

Nice, France

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MM '19 Paper Acceptance Rate 252 of 936 submissions, 27%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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https://dl.acm.org/doi/10.1109/TVCG.2024.3456200
Takagi SNakada Y(2024)Enhancing Formation Analysis Using Multi-layer Graph Convolutional Neural Networks and Geometric Formation Features2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)10.1109/SCISISIS61014.2024.10760047(1-6)Online publication date: 9-Nov-2024
https://doi.org/10.1109/SCISISIS61014.2024.10760047
Dash AYe JWang G(2024)A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote SensingIEEE Access10.1109/ACCESS.2023.334627312(18330-18357)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3346273
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Dossaji TPraneetha BSenthilnathan TSrinivas KHimadhith V(2024)Basket Brain: A Hybrid Model for Basketball Play GenerationInnovative Computing and Communications10.1007/978-981-97-3588-4_26(311-326)Online publication date: 24-Jul-2024
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Chase C(2023)The Data Revolution: Cloud Computing, Artificial Intelligence, and Machine Learning in the Future of Sports21st Century Sports10.1007/978-3-031-38981-8_11(191-205)Online publication date: 29-Dec-2023
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