  Isolating different types of motion in video data is a highly relevant
problem in video analysis. Applications can be found, for example, in dynamic
medical or biological imaging, where the analysis and further processing of the
dynamics of interest is often complicated by additional, unwanted dynamics,
such as motion of the measurement subject. In this work, it is empirically
shown that a representation of video data via untrained generator networks,
together with a specific technique for latent space disentanglement that uses
minimal, one-dimensional information on some of the underlying dynamics, allows
to efficiently isolate different, highly non-linear motion types. In
particular, such a representation allows to freeze any selection of motion
types, and to obtain accurate independent representations of other dynamics of
interest. Obtaining such a representation does not require any pre-training on
a training data set, i.e., all parameters of the generator network are learned
directly from a single video.
