  Synthesizing realistic animations of humans, animals, and even imaginary
creatures, has long been a goal for artists and computer graphics
professionals. Compared to the imaging domain, which is rich with large
available datasets, the number of data instances for the motion domain is
limited, particularly for the animation of animals and exotic creatures (e.g.,
dragons), which have unique skeletons and motion patterns. In this work, we
present a Single Motion Diffusion Model, dubbed SinMDM, a model designed to
learn the internal motifs of a single motion sequence with arbitrary topology
and synthesize motions of arbitrary length that are faithful to them. We
harness the power of diffusion models and present a denoising network
explicitly designed for the task of learning from a single input motion. SinMDM
is designed to be a lightweight architecture, which avoids overfitting by using
a shallow network with local attention layers that narrow the receptive field
and encourage motion diversity. SinMDM can be applied in various contexts,
including spatial and temporal in-betweening, motion expansion, style transfer,
and crowd animation. Our results show that SinMDM outperforms existing methods
both in quality and time-space efficiency. Moreover, while current approaches
require additional training for different applications, our work facilitates
these applications at inference time. Our code and trained models are available
at https://sinmdm.github.io/SinMDM-page.
