Modeling GAN Latent Dynamics using Neural ODEs

In this paper, we propose DynODE, a method 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.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. Our DynODE learns the holistic geometry of the video dynamic space from given sparse observations and specifies continuous latent states, allowing us to engage in various video applications such as frame interpolation and video editing.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.