English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In order to perform unconditional video generation, we must learn thedistribution of the real-world videos. In an effort to synthesize high-qualityvideos, various studies attempted to learn a mapping function between noise andvideos, including recent efforts to separate motion distribution and appearancedistribution. Previous methods, however, learn motion dynamics in discretized,fixed-interval timesteps, which is contrary to the continuous nature of motionof a physical body. In this paper, we propose a novel video generation approachthat learns separate distributions for motion and appearance, the formermodeled by neural ODE to learn natural motion dynamics. Specifically, we employa two-stage approach where the first stage converts a noise vector to asequence of keypoints in arbitrary frame rates, and the second stagesynthesizes videos based on the given keypoints sequence and the appearancenoise vector. Our model not only quantitatively outperforms recent baselinesfor video generation, but also demonstrates versatile functionality such asdynamic frame rate manipulation and motion transfer between two datasets, thusopening new doors to diverse video generation applications.

Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE