IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation

Recent advances in motion-aware large language models haveshown remarkable promise for jointly learning motion understandingand generation knowledge. However, these models typically treat un-derstanding and generation separately, limiting the mutual benefits thatcould arise from interactive feedback between tasks. In this work, wereveal that motion assessment and refinement tasks can act as crucialbridges to enable knowledge flow from motion understanding to genera-tion. Specifically, we propose Interleaved Reasoning for Motion Genera-tion (IRMoGen), a novel paradigm that tightly couples motion genera-tion with assessment and refinement through iterative text-motion dia-logue. To realize this, we introduce IRG-MotionLLM, the first model thatseamlessly interleaves motion generation, assessment, and refinement toimprove the alignment between generated motion and goal text. IRG-MotionLLM is developed progressively with a novel three-stage train-ing scheme, initializing and subsequently enhancing native IRMoGencapabilities. To facilitate this development, we construct an automateddata engine to synthesize interleaved reasoning annotations from existingtext-motion datasets. Extensive experiments demonstrate the propertiesbrought by IRMoGen training, and the advanced cross-benchmark andcross-evaluator performance of IRG-MotionLLM. Code and models areavailable at https://github.com/HumanMLLM/IRG-MotionLLM.