Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases
This work addresses the problem of real-time style control in animation systems for applications like gaming or virtual reality, representing an incremental advancement over prior style transfer methods.
The paper tackles the challenge of real-time style modeling for character animation by introducing a system that uses feature-wise transformations and local motion phases, demonstrating improved robustness and motion quality over existing methods on a new dataset with 100 styles.
Controlling the manner in which a character moves in a real-time animation system is a challenging task with useful applications. Existing style transfer systems require access to a reference content motion clip, however, in real-time systems the future motion content is unknown and liable to change with user input. In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases. An additional style modulation network uses feature-wise transformations to modulate style in real-time. To evaluate our method, we create and release a new style modelling dataset, 100STYLE, containing over 4 million frames of stylised locomotion data in 100 different styles that present a number of challenges for existing systems. To model these styles, we extend the local phase calculation with a contact-free formulation. In comparison to other methods for real-time style modelling, we show our system is more robust and efficient in its style representation while improving motion quality.