Motion Style Slider: novel framework enabling fine control of nuances in character motion

Novel framework enables fine control over the intensity and expressiveness of AI-generated human motion, as reported by researchers from Science Tokyo. The "Motion Style Slider" framework enables animators to control the style and adjust a virtual character’s motion, from subtle to overly exaggerated, based on only two motion samples. The proposed approach achieves consistent style control and smooth transitions without the need for extensive motion-capture datasets.

In the production of videogames and animated movies, directors and animators are constantly fine-tuning how a character’s movements should appear. In many cases, the directors require subtle changes, such as making a character appear a little more restrained or a bit more expressive, and subjectively communicate the degree or extent to which the change needs to be applied. Commercially available artificial intelligence (AI) tools can generate stylized animations for virtual characters, transforming a neutral motion into one that conveys particular styles like joy, anger, exhaustion, or confidence. However, a more refined modulation demands more than simply applying a style; it requires precisely controlling how much of that style comes through.

Most existing tools fall short in this regard, offering only fixed versions of a given emotion or expression with no way to increase or reduce the intensity. The main reason behind this is that AI-based motion-generation systems are often trained using fixed examples of the same action, namely a neutral motion and fully stylized versions obtained through motion-capture technology. Creating additional recordings at multiple intermediate levels of expression would require multiple long motion-capture sessions, making it costly and impractical for real-world production.

To address this problem, a research team comprising Specially Appointed Assistant Professor Chen-Chieh Liao and Professor Hideki Koike of the School of Computing, Institute of Science Tokyo (Science Tokyo), Japan, together with researchers from Cygames, Inc., Japan, has developed the Motion Style Slider, a new AI framework for continuous control of the motion styles intensity in generated human animation. Their work was presented at the European Conference on Computer Vision 2026 (ECCV 2026) held on September 10, 2026.

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• Motion Style Slider: novel framework enabling fine control of nuances in character motion| Science Tokyo News