MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation

2026-05-18

AI Summary

This work addresses the limited ability of existing motion-language models to achieve fine-grained understanding and precise control over body parts, which hinders advancements in animation and interactive applications. To overcome this, we propose MotionMERGE, the first unified multi-granularity motion framework that supports fine-grained language-guided motion generation. By explicitly modeling motion patterns across body parts and temporal dimensions, MotionMERGE enables high-fidelity motion understanding, editing, and synthesis. Key innovations include a joint pretraining strategy—Reasoning-Aware Granularity-Synergy—that aligns representations across granularities while incorporating motion reasoning, a chain-of-thought inference mechanism, and MotionFineEdit, the first large-scale dataset annotated with spatiotemporal correction instructions and reasoning chains. Experiments demonstrate that our approach significantly outperforms current models across multiple tasks and exhibits exceptional zero-shot generalization, advancing the frontier of fine-grained human-motion interaction.

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.

Problem

• fine-grained motion control
• motion-language alignment
• localized motion editing
• temporal grounding
• motion reasoning

Innovation

• fine-grained motion control
• multi-granular modeling
• motion-language alignment
• chain-of-thought reasoning
• spatio-temporal editing