迈向零样本：基于百万级数据的运动生成研究

摘要

基于文本描述生成多样且自然的人体运动序列，构成了计算机视觉、图形学及机器人学领域中一项基础而富有挑战性的研究课题。尽管该领域已取得显著进展，现有方法在零样本泛化能力方面仍面临诸多挑战，这主要归因于训练数据集的规模有限。此外，缺乏全面的评估框架也阻碍了该任务的进一步发展，因其未能明确改进方向。本研究中，我们致力于将文本到运动生成推向一个新时代，即实现零样本泛化能力。为此，我们首先开发了一套高效的标注流程，并推出了MotionMillion——迄今为止最大的人体运动数据集，包含超过2000小时、200万条高质量运动序列。同时，我们提出了MotionMillion-Eval，作为评估零样本运动生成的最全面基准。借助可扩展的架构，我们将模型规模扩展至70亿参数，并在MotionMillion-Eval上验证了其性能。实验结果表明，我们的模型在跨域及复杂组合运动上展现出强大的泛化能力，标志着向零样本人体运动生成迈出了重要一步。相关代码已公开于https://github.com/VankouF/MotionMillion-Codes。

English

Generating diverse and natural human motion sequences based on textual descriptions constitutes a fundamental and challenging research area within the domains of computer vision, graphics, and robotics. Despite significant advancements in this field, current methodologies often face challenges regarding zero-shot generalization capabilities, largely attributable to the limited size of training datasets. Moreover, the lack of a comprehensive evaluation framework impedes the advancement of this task by failing to identify directions for improvement. In this work, we aim to push text-to-motion into a new era, that is, to achieve the generalization ability of zero-shot. To this end, firstly, we develop an efficient annotation pipeline and introduce MotionMillion-the largest human motion dataset to date, featuring over 2,000 hours and 2 million high-quality motion sequences. Additionally, we propose MotionMillion-Eval, the most comprehensive benchmark for evaluating zero-shot motion generation. Leveraging a scalable architecture, we scale our model to 7B parameters and validate its performance on MotionMillion-Eval. Our results demonstrate strong generalization to out-of-domain and complex compositional motions, marking a significant step toward zero-shot human motion generation. The code is available at https://github.com/VankouF/MotionMillion-Codes.

迈向零样本：基于百万级数据的运动生成研究

Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data

摘要

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