Inspired by the recent success of LLMs, the field of human motion understanding has increasingly shifted towards the development of large motion models. Despite some progress, current state-of-the-art works remain far from achieving truly generalist models, largely due to the lack of large-scale, high-quality motion data. To address this, we present MotionBase, the first million-level motion generation benchmark, offering 15 times the data volume of the previous largest dataset, and featuring multimodal data with hierarchically detailed text descriptions. By leveraging this vast dataset, our large motion model demonstrates strong performance across a broad range of motions, including unseen ones. Through systematic investigation, we underscore the importance of scaling both data and model size, with synthetic data and pseudo labels playing a crucial role in mitigating data acquisition costs. Moreover, our research reveals the limitations of existing evaluation metrics, particularly in handling out-of-domain text instructions -- an issue that has long been overlooked. In addition to these, we introduce a novel 2D lookup-free approach for motion tokenization, which preserves motion information and expands codebook capacity, further enhancing the representative ability of large motion models. The release of MotionBase and the insights gained from this study are expected to pave the way for the development of more powerful and versatile motion generation models.

本研究针对当前人类运动理解领域中缺乏大规模高质量运动数据的问题，提出了首个百万级运动生成基准MotionBase，数据量是之前最大数据集的15倍，包含多模态数据和分层详细文本描述。研究表明，数据和模型规模的扩展至关重要，且现有评估指标在处理域外文本指令方面存在不足，为运动生成模型的进一步发展提供了重要的见解和基础。

何处去，运动生成？从大型语言模型到大型运动模型