An autoregressive generation model for producing instant basketball defensive trajectory

Huan Hua Chang, Wen Cheng Chen, Wan Lun Tsai, Min Chun Hu, Wei Ta Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Learning basketball tactic via virtual reality environment requires real-time feedback to improve the realism and interactivity. For example, the virtual defender should move immediately according to the player's movement. In this paper, we proposed an autoregressive generative model for basketball defensive trajectory generation. To learn the continuous Gaussian distribution of player position, we adopt a differentiable sampling process to sample the candidate location with a standard deviation loss, which can preserve the diversity of the trajectories. Furthermore, we design several additional loss functions based on the domain knowledge of basketball to make the generated trajectories match the real situation in basketball games. The experimental results show that the proposed method can achieve better performance than previous works in terms of different evaluation metrics.

Original languageEnglish
Title of host publicationProceedings of the 2nd ACM International Conference on Multimedia in Asia, MMAsia 2020
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450383080
DOIs
Publication statusPublished - 2021 Mar 7
Event2nd ACM International Conference on Multimedia in Asia, MMAsia 2020 - Virtual, Online, Singapore
Duration: 2021 Mar 7 → …

Publication series

NameProceedings of the 2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

Conference

Conference2nd ACM International Conference on Multimedia in Asia, MMAsia 2020
Country/TerritorySingapore
CityVirtual, Online
Period21-03-07 → …

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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