用视频库增强动作大模型，让文字生成动作更自然准确
VimoRAG: Video-based Retrieval-augmented 3D Motion Generation for Motion Language Models
- 从真实视频中检索相关人体动作信号，补充文本输入的不足
- 在动作识别任务上提升12.7%的准确率，显著改善生成质量
- 适合研究动作生成、多模态模型或数字人动画的开发者
本文提出VimoRAG，一种基于视频的检索增强型3D动作生成框架，用于提升动作大语言模型（Motion LLMs）的性能。由于标注数据有限，动作大模型常面临域外/词汇外问题。VimoRAG利用大规模真实场景视频数据库，通过检索相关2D人体运动信号来增强3D动作生成能力。针对视频驱动动作检索的挑战，我们解决了两大瓶颈：(1) 构建以动作为中心的视频检索模型，精准区分人体姿态与动作类别；(2) 缓解因检索不准确导致的误差传播。为此，我们设计了Gemini Motion Video Retriever和Motion-centric Dual-alignment DPO Trainer，实现高效检索与生成。实验表明，相较于仅依赖文本输入的动作大模型，VimoRAG显著提升性能。所有资源详见https://walkermitty.github.io/VimoRAG/
原文摘要 · Abstract (English)
This paper introduces VimoRAG, a novel video-based retrieval-augmented motion generation framework for motion large language models (LLMs). As motion LLMs face severe out-of-domain/out-of-vocabulary issues due to limited annotated data, VimoRAG leverages large-scale in-the-wild video databases to enhance 3D motion generation by retrieving relevant 2D human motion signals. While video-based motion RAG is nontrivial, we address two key bottlenecks: (1) developing an effective motion-centered video retrieval model that distinguishes human poses and actions, and (2) mitigating the issue of error propagation caused by suboptimal retrieval results. We design the Gemini Motion Video Retriever mechanism and the Motion-centric Dual-alignment DPO Trainer, enabling effective retrieval and generation processes. Experimental results show that VimoRAG significantly boosts the performance of motion LLMs constrained to text-only input. All the resources are available at https://walkermitty.github.io/VimoRAG/
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