AToM: Aligning Text-to-Motion Model at Event-Level with GPT-4Vision Reward

• URL: http://arxiv.org/abs/2411.18654v1
• Date: Wed, 27 Nov 2024 05:32:18 GMT
• Title: AToM: Aligning Text-to-Motion Model at Event-Level with GPT-4Vision Reward
• Authors: Haonan Han, Xiangzuo Wu, Huan Liao, Zunnan Xu, Zhongyuan Hu, Ronghui Li, Yachao Zhang, Xiu Li,
• Abstract summary: AToM is a framework that enhances the alignment between generated motion and text prompts by leveraging reward from GPT-4Vision. AToM significantly improves the event-level alignment quality of text-to-motion generation.
• Score: 20.249532310376026
• License:
• Abstract: Recently, text-to-motion models have opened new possibilities for creating realistic human motion with greater efficiency and flexibility. However, aligning motion generation with event-level textual descriptions presents unique challenges due to the complex relationship between textual prompts and desired motion outcomes. To address this, we introduce AToM, a framework that enhances the alignment between generated motion and text prompts by leveraging reward from GPT-4Vision. AToM comprises three main stages: Firstly, we construct a dataset MotionPrefer that pairs three types of event-level textual prompts with generated motions, which cover the integrity, temporal relationship and frequency of motion. Secondly, we design a paradigm that utilizes GPT-4Vision for detailed motion annotation, including visual data formatting, task-specific instructions and scoring rules for each sub-task. Finally, we fine-tune an existing text-to-motion model using reinforcement learning guided by this paradigm. Experimental results demonstrate that AToM significantly improves the event-level alignment quality of text-to-motion generation.

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