MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
MotionMERGE is a unified framework that bridges the granularity gap in motion-language models by explicitly modeling motion at part and temporal levels within a single LLM. It introduces Reasoning-Aware Granularity-Synergy pre-training and the MotionFineEdit dataset (981K triplets) with fine-grained corrective instructions and chain-of-thought annotations. Experiments demonstrate superior precision in motion generation, understanding, and editing, with compelling zero-shot generalization.
[2605.18956] MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
[Submitted on 18 May 2026]
Title:MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
View a PDF of the paper titled MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation, by Bizhu Wu and 7 other authors
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Abstract:Recent motion-language models unify tasks like comprehension and generation but operate at a coarse granularity, lacking fine-grained understanding and nuanced control over body parts needed for animation or interaction. This stems from fundamental issues in both the model and the data, in which the model can't focus on motion's localized pattern, and the training data lacks fine-grained supervision. To tackle this, we propose MotionMERGE, a unified framework that bridges the granularity gap. First, we pioneer the study of fine-grained languageguided motion control, including detailed understanding and localized editing, by explicitly modeling motion at part and temporal levels within a single LLM, thereby endowing the model with robust priors for precise control. Second, we design ReasoningAware Granularity-Synergy pre-training, a novel strategy that employs joint supervision for cross-granularity alignment, temporal grounding, localized alignment, motion coherency, and motion-grounded chain-of-thought (CoT) reasoning. This equips the model with fine-grained motion-language alignment, crossgranularity synergy, and explicit reasoning ability. Third, we curate MotionFineEdit, a large-scale dataset (837K atomic + 144K complex triplets) with the first fine-grained spatio-temporal corrective instructions and motion-grounded CoT annotations, establishing a new benchmark for fine-grained text-driven motion editing and motion-grounded reasoning. Extensive experiments demonstrate the capability of MotionMERGE for more precise motion generation, understanding, and editing, and compelling zero-shot generalization to other complex motion tasks. This work represents a significant step toward models that interact with motion in finer granularity and human-like reasoning.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.18956 [cs.CV]
(or arXiv:2605.18956v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.18956
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Bizhu Wu [view email] [v1] Mon, 18 May 2026 18:00:04 UTC (20,301 KB)
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