  In this work, we introduce an unconditional video generative model,
InMoDeGAN, targeted to (a) generate high quality videos, as well as to (b)
allow for interpretation of the latent space. For the latter, we place emphasis
on interpreting and manipulating motion. Towards this, we decompose motion into
semantic sub-spaces, which allow for control of generated samples. We design
the architecture of InMoDeGAN-generator in accordance to proposed Linear Motion
Decomposition, which carries the assumption that motion can be represented by a
dictionary, with related vectors forming an orthogonal basis in the latent
space. Each vector in the basis represents a semantic sub-space. In addition, a
Temporal Pyramid Discriminator analyzes videos at different temporal
resolutions. Extensive quantitative and qualitative analysis shows that our
model systematically and significantly outperforms state-of-the-art methods on
the VoxCeleb2-mini and BAIR-robot datasets w.r.t. video quality related to (a).
Towards (b) we present experimental results, confirming that decomposed
sub-spaces are interpretable and moreover, generated motion is controllable.
