  We present GANimator, a generative model that learns to synthesize novel
motions from a single, short motion sequence. GANimator generates motions that
resemble the core elements of the original motion, while simultaneously
synthesizing novel and diverse movements. Existing data-driven techniques for
motion synthesis require a large motion dataset which contains the desired and
specific skeletal structure. By contrast, GANimator only requires training on a
single motion sequence, enabling novel motion synthesis for a variety of
skeletal structures e.g., bipeds, quadropeds, hexapeds, and more. Our framework
contains a series of generative and adversarial neural networks, each
responsible for generating motions in a specific frame rate. The framework
progressively learns to synthesize motion from random noise, enabling
hierarchical control over the generated motion content across varying levels of
detail. We show a number of applications, including crowd simulation, key-frame
editing, style transfer, and interactive control, which all learn from a single
input sequence. Code and data for this paper are at
https://peizhuoli.github.io/ganimator.
