  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/
