ReMoMask: Retrieval-Augmented Masked Motion Generation

Retrieval-Augmented Text-to-Motion (RAG-T2M) modelshave demonstrated superior performance over conventional T2M ap-proaches, particularly in handling uncommon and complex textual de-scriptions by leveraging external motion knowledge. Despite these gains,existing RAG-T2M models remain limited by two closely related factors:coarse-grained text-motion retrieval that overlooks the hierarchical struc-ture of human motion, and underexplored mechanisms for effectivelyfusing retrieved information into the generative process. In this work,we present ReMoMask, a structure-aware RAG framework for text-to-motion generation that addresses these limitations. To improve retrieval,we propose Hierarchical Bidirectional Momentum (HBM) contrastivelearning, which employs dual objectives to jointly align global motionsemantics and fine-grained part-level features with text. To bridge thegap between structured retrieval and generation, we introduce TopologyStructured Masking (TSM), a training strategy that adaptively masksmotion tokens based on semantic relevance, forcing the model to learnrobust part-level grounding. Furthermore, we design Semantic Spatial-Temporal Attention (SSTA), a topology-aware fusion module that inte-grates retrieved knowledge via an asymmetric attention mechanism. Ex-tensive experiments on HumanML3D, KIT-ML, and SnapMoGen demon-strate that ReMoMask consistently outperforms prior methods on bothtext-motion retrieval and text-to-motion generation benchmarks.