MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities

• URL: http://arxiv.org/abs/2504.02478v1
• Date: Thu, 03 Apr 2025 10:53:41 GMT
• Title: MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities
• Authors: Bizhu Wu, Jinheng Xie, Keming Shen, Zhe Kong, Jianfeng Ren, Ruibin Bai, Rong Qu, Linlin Shen,
• Abstract summary: We pioneer MG-MotionLLM, a unified motion-language model for multi-granular motion comprehension and generation.<n>We introduce a comprehensive multi-granularity training scheme by incorporating a set of novel auxiliary tasks.<n>Our MG-MotionLLM achieves superior performance on classical text-to-motion and motion-to-text tasks.
• Score: 36.42160163142448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent motion-aware large language models have demonstrated promising potential in unifying motion comprehension and generation. However, existing approaches primarily focus on coarse-grained motion-text modeling, where text describes the overall semantics of an entire motion sequence in just a few words. This limits their ability to handle fine-grained motion-relevant tasks, such as understanding and controlling the movements of specific body parts. To overcome this limitation, we pioneer MG-MotionLLM, a unified motion-language model for multi-granular motion comprehension and generation. We further introduce a comprehensive multi-granularity training scheme by incorporating a set of novel auxiliary tasks, such as localizing temporal boundaries of motion segments via detailed text as well as motion detailed captioning, to facilitate mutual reinforcement for motion-text modeling across various levels of granularity. Extensive experiments show that our MG-MotionLLM achieves superior performance on classical text-to-motion and motion-to-text tasks, and exhibits potential in novel fine-grained motion comprehension and editing tasks. Project page: CVI-SZU/MG-MotionLLM

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