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arXiv Computer Vision By Chen-Chieh Liao, Yichen Peng, Yiyi Cai, Y\^ui Ono, Hiroki Hanaoka, Erwin Wu, Hideki Koike, Shuichi Kurabayashi

Motion Style Slider: Endpoint-Supervised Continuous Style Control for Human Motion Diffusion

Read the original on arXiv Computer Vision →

The paper introduces Motion Style Slider, a framework that enables continuous, endpoint‑supervised control of style intensity in human motion diffusion. By constructing a style direction in a learned motion‑style embedding space and conditioning diffusion generation with a scalar intensity, the method achieves smooth, monotonic style scaling without needing intermediate‑intensity ground truth. The approach is compatible with pretrained diffusion backbones, supports heterogeneous style datasets, and is evaluated on controllability, interpolation/extrapolation, content preservation, and motion realism.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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