  Motion style transfer is highly desired for motion generation systems for
gaming. Compared to its offline counterpart, the research on online motion
style transfer under interactive control is limited. In this work, we propose
an end-to-end neural network that can generate motions with different styles
and transfer motion styles in real-time under user control. Our approach
eliminates the use of handcrafted phase features, and could be easily trained
and directly deployed in game systems. In the experiment part, we evaluate our
approach from three aspects that are essential for industrial game design:
accuracy, flexibility, and variety, and our model performs a satisfying result.
