STyMo: Fast and Controllable Few-Shot Motion Style Transfer
Supporting a wide variety of motion styles is critical for creating diverse virtual characters, but current methods either require large stylized datasets or pre-trained models that cannot generalize beyond their training distribution. We present STyMo, a few-shot approach that learns motion style from only seconds of paired data and trains in one to two minutes. Our key insight is to decompose style into two components: a static component capturing time-invariant posture, and a temporal component capturing frame-wise dynamics. This decomposition yields an interpretable system where posture intensity, temporal exaggeration, and per-body-region style can be adjusted at runtime. Furthermore...
| Authors | Jose Luis Ponton, Alexander Winkler, Ladislav Kavan et al. |
|---|---|
| Primary category | cs.GR |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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