In-Place Gestures Classification via Long-term Memory Augmented Network

@article{Zhao2022InPlaceGC,
  title={In-Place Gestures Classification via Long-term Memory Augmented Network},
  author={Lizhi Zhao and Xuequan Lu and Qianyue Bao and Meili Wang},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.01059}
}
In-place gesture-based virtual locomotion techniques enable users to control their viewpoint and intuitively move in the 3D virtual environment. A key research problem is to accurately and quickly recognize in-place gestures, since they can trigger specific movements of virtual viewpoints and enhance user experience. However, to achieve real-time experience, only short-term sensor sequence data (up to about 300ms, 6 to 10 frames) can be taken as input, which actually affects the classification… 

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