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

We deal with the controllable person image synthesis task which aims tore-render a human from a reference image with explicit control over body poseand appearance. Observing that person images are highly structured, we proposeto generate desired images by extracting and distributing semantic entities ofreference images. To achieve this goal, a neural texture extraction anddistribution operation based on double attention is described. This operationfirst extracts semantic neural textures from reference feature maps. Then, itdistributes the extracted neural textures according to the spatialdistributions learned from target poses. Our model is trained to predict humanimages in arbitrary poses, which encourages it to extract disentangled andexpressive neural textures representing the appearance of different semanticentities. The disentangled representation further enables explicit appearancecontrol. Neural textures of different reference images can be fused to controlthe appearance of the interested areas. Experimental comparisons show thesuperiority of the proposed model. Code is available athttps://github.com/RenYurui/Neural-Texture-Extraction-Distribution.

Neural Texture Extraction and Distribution for Controllable Person Image Synthesis