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

Each smile is unique: one person surely smiles in different ways (e.g.,closing/opening the eyes or mouth). Given one input image of a neutral face,can we generate multiple smile videos with distinctive characteristics? Totackle this one-to-many video generation problem, we propose a novel deeplearning architecture named Conditional Multi-Mode Network (CMM-Net). To betterencode the dynamics of facial expressions, CMM-Net explicitly exploits faciallandmarks for generating smile sequences. Specifically, a variationalauto-encoder is used to learn a facial landmark embedding. This singleembedding is then exploited by a conditional recurrent network which generatesa landmark embedding sequence conditioned on a specific expression (e.g.,spontaneous smile). Next, the generated landmark embeddings are fed into amulti-mode recurrent landmark generator, producing a set of landmark sequencesstill associated to the given smile class but clearly distinct from each other.Finally, these landmark sequences are translated into face videos. Ourexperimental results demonstrate the effectiveness of our CMM-Net in generatingrealistic videos of multiple smile expressions.

Every Smile is Unique: Landmark-Guided Diverse Smile Generation