MixiMotion: One-Step Text-to-Motion Generation via Asymmetric Set Distillation
Iterative text-to-motion generation delivers high-quality and semantically aligned motions but requires multiple network evaluations, resulting in substantial inference latency. We present \textbf{MixiMotion}, a strict one-step text-to-motion generation framework based on offline set distillation. Instead of distilling a single teacher trajectory for each text prompt, MixiMotion constructs an offline bank of multiple teacher motions and aligns teacher and student sample sets through \textbf{asymmetric bidirectional matching}. The teacher-to-student direction promotes coverage of diverse teacher-supported motions, while the student-to-teacher direction suppresses unsupported generations. W...
| Authors | Hung Dinh, Binh Mai, Tran Quoc Bao Le et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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