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

A recent paper by Gatys et al. describes a method for rendering an image inthe style of another image. First, they use convolutional neural networkfeatures to build a statistical model for the style of an image. Then theycreate a new image with the content of one image but the style statistics ofanother image. Here, we extend this method to render a movie in a givenartistic style. The naive solution that independently renders each frameproduces poor results because the features of the style move substantially fromone frame to the next. The other naive method that initializes the optimizationfor the next frame using the rendered version of the previous frame alsoproduces poor results because the features of the texture stay fixed relativeto the frame of the movie instead of moving with objects in the scene. The maincontribution of this paper is to use optical flow to initialize the styletransfer optimization so that the texture features move with the objects in thevideo. Finally, we suggest a method to incorporate optical flow explicitly intothe cost function.

DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies