AI RESEARCH
UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors
arXiv CS.CV
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ArXi:2603.15975v1 Announce Type: new Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets and paired text descriptions. However, how to effectively and efficiently leverage such single-purpose motion LFMs, i.e., text-to-motion synthesis, in diverse cross-modal and in-context motion generation downstream tasks remains largely unclear. Prior work typically adapts pretrained generative priors to individual downstream tasks in a task-specific manner.