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BasketballGAN

Generate the ghosting defensive strategies given offensive sketch.


BasketballGAN: Generating Basketball Play Simulation through Sketching

Hsin-Ying Hsieh1, Chieh-Yu Chen2, Yu-Shuen Wang1 and Jung-Hong Chuang1

1National Chiao Tung University,

2NVIDIA Corporation

Accepted paper in ACMMM 2019.

Prerequisites

Getting Stated

~$ git clone https://github.com/chychen/BasketballGAN.git
~$ cd BasketballGAN
BasketballGAN$ docker login nvcr.io
BasketballGAN$ docker pull nvcr.io/nvidia/tensorflow:19.06-py2
BasketballGAN$ docker run --runtime=nvidia -it --rm -v $PWD:$PWD --net host nvcr.io/nvidia/tensorflow:19.06-py2 bash
root@c63207c81408:~/BasketballGAN$ apt update
root@c63207c81408:~/BasketballGAN$ apt install ffmpeg

Download Dataset

  • create 'data' folder
  • save dataset under folder 'data'
BasketballGAN$ mkdir data

Training

BasketballGAN$ cd src
BasketballGAN/src$ python Train_Triple.py --folder_path='tmp' --data_path='data'

Logs/Samples/Checkpoints

- "BasketballGAN/src/tmp/Log": training summary for tensorboard.
- "BasketballGAN/src/tmp/Samples": generated videos sampled on different epoches.
- "BasketballGAN/src/tmp/Checkpoints": tensorflow checkpoints on different iterations.

Monitoring

BasketballGAN/src$ python -m http.server 8000
BasketballGAN/src$ tensorboard --logdir='tmp/Log'

Public Relations

Citation

If you find this useful for your research, please use the following.

@article{hsieh2019basketballgan,
  title={BasketballGAN: Generating Basketball Play Simulation Through Sketching},
  author={Hsieh, Hsin-Ying and Chen, Chieh-Yu and Wang, Yu-Shuen and Chuang, Jung-Hong},
  journal={arXiv preprint arXiv:1909.07088},
  year={2019}
}

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Basketball coaches often sketch plays on a whiteboard to help players get the ball through the net. A new AI model predicts how opponents would respond to these tactics.

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