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

Text-driven human motion generation in computer vision is both significantand challenging. However, current methods are limited to producing eitherdeterministic or imprecise motion sequences, failing to effectively control thetemporal and spatial relationships required to conform to a given textdescription. In this work, we propose a fine-grained method for generatinghigh-quality, conditional human motion sequences supporting precise textdescription. Our approach consists of two key components: 1) alinguistics-structure assisted module that constructs accurate and completelanguage feature to fully utilize text information; and 2) a context-awareprogressive reasoning module that learns neighborhood and overall semanticlinguistics features from shallow and deep graph neural networks to achieve amulti-step inference. Experiments show that our approach outperformstext-driven motion generation methods on HumanML3D and KIT test sets andgenerates better visually confirmed motion to the text conditions.

Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model