  In this paper, we propose to model the video dynamics by learning the
trajectory of independently inverted latent codes from GANs. The entire
sequence is seen as discrete-time observations of a continuous trajectory of
the initial latent code, by considering each latent code as a moving particle
and the latent space as a high-dimensional dynamic system. The latent codes
representing different frames are therefore reformulated as state transitions
of the initial frame, which can be modeled by neural ordinary differential
equations. The learned continuous trajectory allows us to perform infinite
frame interpolation and consistent video manipulation. The latter task is
reintroduced for video editing with the advantage of requiring the core
operations to be applied to the first frame only while maintaining temporal
consistency across all frames. Extensive experiments demonstrate that our
method achieves state-of-the-art performance but with much less computation.
Code is available at https://github.com/weihaox/dynode_released.
