Wu, ZiyuMichel, ThomasRohmer, DamienCeylan, DuyguLi, Tzu-Mao2025-05-092025-05-092025978-3-03868-268-41017-4656https://doi.org/10.2312/egs.20251048https://diglib.eg.org/handle/10.2312/egs20251048We present a lightweight method for encoding, learning, and predicting 3D rigged character motion sequences that consider both the character's pose and morphology. Specifically, we introduce an enhanced skeletal embedding that extends the standard skeletal representation by incorporating the radius of proxy cylinders, which conveys geometric information about the character's morphology at each joint. This additional geometric data is represented using compact tokens designed to work seamlessly with transformer architectures. This simple yet effective representation demonstrated through three distinct tokenization strategies, maintains the efficiency of skeletal-based representations while enhancing the accuracy of motion sequence predictions across diverse morphologies. Notably, our method achieves these results despite being trained on a limited dataset, showcasing its potential for applications with scarce animation data.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → AnimationComputing methodologies → Animation10.2312/egs.202510484 pages