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Extended Reality is a revolutionary method of delivering multimedia contentto users. A large contributor to its popularity is the sense of immersion andinteractivity enabled by having real-world motion reflected in the virtualexperience accurately and immediately. This user motion, mainly caused by headrotations, induces several technical challenges. For instance, which content isgenerated and transmitted depends heavily on where the user is looking.Seamless systems, taking user motion into account proactively, will thereforerequire accurate predictions of upcoming rotations. Training and evaluatingsuch predictors requires vast amounts of orientational input data, which isexpensive to gather, as it requires human test subjects. A more feasibleapproach is to gather a modest dataset through test subjects, and then extendit to a more sizeable set using synthetic data generation methods. In thiswork, we present a head rotation time series generator based on TimeGAN, anextension of the well-known Generative Adversarial Network, designedspecifically for generating time series. This approach is able to extend adataset of head rotations with new samples closely matching the distribution ofthe measured time series.

Generating Realistic Synthetic Head Rotation Data for Extended Reality using Deep Learning