STyMo: Fast and Controllable Few-Shot Motion Style Transfer

Abstract

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, the reduction in required training data and computation time structurally permits an iterative authoring workflow. To ensure robustness on arbitrary inputs, we further introduce a stylizability gate that automatically prevents artifacts on out-of-distribution motions. We demonstrate results across diverse motion styles, from subtle emotional variations to exaggerated character archetypes, and release our processed paired dataset to facilitate future research.

Project webpage: https://joseluisponton.com/stymo-project-page/

References in corpus (8)

• Character Controllers Using Motion VAEs
• Skeleton-Aware Networks for Deep Motion Retargeting
• Unpaired Motion Style Transfer from Video to Animation
• Combining Motion Matching and Orientation Prediction to Animate Avatars for Consumer-Grade VR Devices
• MOCHA: Real-Time Motion Characterization via Context Matching
• WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds
• Decoupling Contact for Fine-Grained Motion Style Transfer
• Environment-aware Motion Matching