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

Video generation remains a challenging task due to spatiotemporal complexityand the requirement of synthesizing diverse motions with temporal consistency.Previous works attempt to generate videos in arbitrary lengths either in anautoregressive manner or regarding time as a continuous signal. However, theystruggle to synthesize detailed and diverse motions with temporal coherence andtend to generate repetitive scenes after a few time steps. In this work, weargue that a single time-agnostic latent vector of style-based generator isinsufficient to model various and temporally-consistent motions. Hence, weintroduce additional time-dependent motion styles to model diverse motionpatterns. In addition, a Motion Style Attention modulation mechanism, dubbed asMoStAtt, is proposed to augment frames with vivid dynamics for each specificscale (i.e., layer), which assigns attention score for each motion style w.r.tdeconvolution filter weights in the target synthesis layer and softly attendsdifferent motion styles for weight modulation. Experimental results show ourmodel achieves state-of-the-art performance on four unconditional $256^2$ videosynthesis benchmarks trained with only 3 frames per clip and produces betterqualitative results with respect to dynamic motions. Code and videos have beenmade available at https://github.com/xiaoqian-shen/MoStGAN-V.

MoStGAN-V: Video Generation with Temporal Motion Styles