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 this paper, we present SignAvatars, the first large-scale multi-prompt 3Dsign language (SL) motion dataset designed to bridge the communication gap forhearing-impaired individuals. While there has been an exponentially growingnumber of research regarding digital communication, the majority of existingcommunication technologies primarily cater to spoken or written languages,instead of SL, the essential communication method for hearing-impairedcommunities. Existing SL datasets, dictionaries, and sign language production(SLP) methods are typically limited to 2D as the annotating 3D models andavatars for SL is usually an entirely manual and labor-intensive processconducted by SL experts, often resulting in unnatural avatars. In response tothese challenges, we compile and curate the SignAvatars dataset, whichcomprises 70,000 videos from 153 signers, totaling 8.34 million frames,covering both isolated signs and continuous, co-articulated signs, withmultiple prompts including HamNoSys, spoken language, and words. To yield 3Dholistic annotations, including meshes and biomechanically-valid poses of body,hands, and face, as well as 2D and 3D keypoints, we introduce an automatedannotation pipeline operating on our large corpus of SL videos. SignAvatarsfacilitates various tasks such as 3D sign language recognition (SLR) and thenovel 3D SL production (SLP) from diverse inputs like text scripts, individualwords, and HamNoSys notation. Hence, to evaluate the potential of SignAvatars,we further propose a unified benchmark of 3D SL holistic motion production. Webelieve that this work is a significant step forward towards bringing thedigital world to the hearing-impaired communities. Our project page is athttps://signavatars.github.io/

SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark