  In the deep learning era, long video generation of high-quality still remains
challenging due to the spatio-temporal complexity and continuity of videos.
Existing prior works have attempted to model video distribution by representing
videos as 3D grids of RGB values, which impedes the scale of generated videos
and neglects continuous dynamics. In this paper, we found that the recent
emerging paradigm of implicit neural representations (INRs) that encodes a
continuous signal into a parameterized neural network effectively mitigates the
issue. By utilizing INRs of video, we propose dynamics-aware implicit
generative adversarial network (DIGAN), a novel generative adversarial network
for video generation. Specifically, we introduce (a) an INR-based video
generator that improves the motion dynamics by manipulating the space and time
coordinates differently and (b) a motion discriminator that efficiently
identifies the unnatural motions without observing the entire long frame
sequences. We demonstrate the superiority of DIGAN under various datasets,
along with multiple intriguing properties, e.g., long video synthesis, video
extrapolation, and non-autoregressive video generation. For example, DIGAN
improves the previous state-of-the-art FVD score on UCF-101 by 30.7% and can be
trained on 128 frame videos of 128x128 resolution, 80 frames longer than the 48
frames of the previous state-of-the-art method.
