In the realm of motion generation, the creation of long-duration, high-quality motion sequences remains a significant challenge. This paper presents our groundbreaking work on "Infinite Motion", a novel approach that leverages long text to extended motion generation, effectively bridging the gap between short and long-duration motion synthesis. Our core insight is the strategic extension and reassembly of existing high-quality text-motion datasets, which has led to the creation of a novel benchmark dataset to facilitate the training of models for extended motion sequences. A key innovation of our model is its ability to accept arbitrary lengths of text as input, enabling the generation of motion sequences tailored to specific narratives or scenarios. Furthermore, we incorporate the timestamp design for text which allows precise editing of local segments within the generated sequences, offering unparalleled control and flexibility in motion synthesis. We further demonstrate the versatility and practical utility of "Infinite Motion" through three specific applications: natural language interactive editing, motion sequence editing within long sequences and splicing of independent motion sequences. Each application highlights the adaptability of our approach and broadens the spectrum of possibilities for research and development in motion generation. Through extensive experiments, we demonstrate the superior performance of our model in generating long sequence motions compared to existing methods.Project page: https://shuochengzhai.github.io/Infinite-motion.github.io/

翻译：在运动生成领域，创建长时长、高质量的运动序列仍然是一个重大挑战。本文介绍了我们在"无限运动"方面的突破性工作，这是一种利用长文本进行扩展运动生成的新方法，有效弥合了短时长与长时长运动合成之间的差距。我们的核心洞见在于对现有高质量文本-运动数据集进行策略性扩展与重组，从而创建了一个新颖的基准数据集，以促进扩展运动序列模型的训练。我们模型的一个关键创新在于能够接受任意长度的文本输入，从而生成符合特定叙事或场景的运动序列。此外，我们引入了文本时间戳设计，允许对生成序列中的局部片段进行精确编辑，为运动合成提供了无与伦比的控制力和灵活性。我们进一步通过三个具体应用展示了"无限运动"的多样性与实用价值：自然语言交互式编辑、长序列内的运动片段编辑以及独立运动序列的拼接。每个应用都凸显了我们方法的适应性，并拓宽了运动生成研究与开发的可能性空间。通过大量实验，我们证明了相较于现有方法，我们的模型在生成长序列运动方面具有更优越的性能。项目页面：https://shuochengzhai.github.io/Infinite-motion.github.io/