We introduce InfiniDreamer, a novel framework for arbitrarily long human motion generation. Existing motion generation methods are often constrained to short sequences due to the lack of long motion training data. To overcome this, InfiniDreamer first generates sub-motions corresponding to each textual description and assembles them into a coarse long sequence using randomly initialized transition segments. To refine the entire motion, we propose Segment Score Distillation (SSD)—an optimization-based method that leverages a motion prior trained solely on short clips, enabling long-sequence generation without additional training. 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.