20 research outputs found
Latent Neural Differential Equations for Video Generation
Generative Adversarial Networks have recently shown promise for video
generation, building off of the success of image generation while also
addressing a new challenge: time. Although time was analyzed in some early
work, the literature has not adequately grown with temporal modeling
developments. We propose studying the effects of Neural Differential Equations
to model the temporal dynamics of video generation. The paradigm of Neural
Differential Equations presents many theoretical strengths including the first
continuous representation of time within video generation. In order to address
the effects of Neural Differential Equations, we will investigate how changes
in temporal models affect generated video quality