We present InfiniDreamer, a novel framework for arbitrarily long human motion generation. InfiniDreamer addresses the limitations of current motion generation methods, which are typically restricted to short sequences due to the lack of long motion training data. To achieve this, we first generate sub-motions corresponding to each textual description and then assemble them into a coarse, extended sequence using randomly initialized transition segments. We then introduce an optimization-based method called Segment Score Distillation (SSD) to refine the entire long motion sequence. SSD is designed to utilize an existing motion prior, which is trained only on short clips, in a training-free manner. Specifically, SSD iteratively refines overlapping short segments sampled from the coarsely extended long motion sequence, progressively aligning them with the pre-trained motion diffusion prior. This process ensures local coherence within each segment, while the refined transitions between segments maintain global consistency across the entire sequence. Extensive qualitative and quantitative experiments validate the superiority of our framework, showcasing its ability to generate coherent, contextually aware motion sequences of arbitrary length.

本研究针对当前运动生成方法的局限性，即短序列生成，由于缺乏长时间运动训练数据而受到限制。提出的InfiniDreamer框架通过生成与文本描述对应的子运动，并利用片段评分蒸馏（SSD）方法优化长序列，能够生成一致且具上下文意识的任意长度运动序列。大量实验表明，该方法在生成运动序列的能力上优于现有方法。

InfiniDreamer：通过片段评分蒸馏实现任意长度的人类运动生成