TY - GEN
T1 - Diverse generation for multi-agent sports games
AU - Yeh, Raymond A.
AU - Schwing, Alexander G.
AU - Huang, Jonathan
AU - Murphy, Kevin
N1 - Acknowledgments: This work is supported in part by NSF under Grant No. 1718221, Samsung, 3M and a Google PhD Fellowship to RY. We thank NVIDIA for providing GPUs used for this work.
PY - 2019/6
Y1 - 2019/6
N2 - In this paper, we propose a new generative model for multi-agent trajectory data, focusing on the case of multi-player sports games. Our model leverages graph neural networks (GNNs) and variational recurrent neural networks (VRNNs) to achieve a permutation equivariant model suitable for sports. On two challenging datasets (basketball and soccer), we show that we are able to produce more accurate forecasts than previous methods. We assess accuracy using various metrics, such as log-likelihood and 'best of N' loss, based on N different samples of the future. We also measure the distribution of statistics of interest, such as player location or velocity, and show that the distribution induced by our generative model better matches the empirical distribution of the test set.
AB - In this paper, we propose a new generative model for multi-agent trajectory data, focusing on the case of multi-player sports games. Our model leverages graph neural networks (GNNs) and variational recurrent neural networks (VRNNs) to achieve a permutation equivariant model suitable for sports. On two challenging datasets (basketball and soccer), we show that we are able to produce more accurate forecasts than previous methods. We assess accuracy using various metrics, such as log-likelihood and 'best of N' loss, based on N different samples of the future. We also measure the distribution of statistics of interest, such as player location or velocity, and show that the distribution induced by our generative model better matches the empirical distribution of the test set.
KW - Deep Learning
KW - Motion and Tracking
UR - https://www.scopus.com/pages/publications/85077759711
UR - https://www.scopus.com/pages/publications/85077759711#tab=citedBy
U2 - 10.1109/CVPR.2019.00474
DO - 10.1109/CVPR.2019.00474
M3 - Conference contribution
AN - SCOPUS:85077759711
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 4605
EP - 4614
BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PB - IEEE Computer Society
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Y2 - 16 June 2019 through 20 June 2019
ER -