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

Generating natural human motion from a story has the potential to transformthe landscape of animation, gaming, and film industries. A new and challengingtask, Story-to-Motion, arises when characters are required to move to variouslocations and perform specific motions based on a long text description. Thistask demands a fusion of low-level control (trajectories) and high-levelcontrol (motion semantics). Previous works in character control andtext-to-motion have addressed related aspects, yet a comprehensive solutionremains elusive: character control methods do not handle text description,whereas text-to-motion methods lack position constraints and often produceunstable motions. In light of these limitations, we propose a novel system thatgenerates controllable, infinitely long motions and trajectories aligned withthe input text. (1) We leverage contemporary Large Language Models to act as atext-driven motion scheduler to extract a series of (text, position, duration)pairs from long text. (2) We develop a text-driven motion retrieval scheme thatincorporates motion matching with motion semantic and trajectory constraints.(3) We design a progressive mask transformer that addresses common artifacts inthe transition motion such as unnatural pose and foot sliding. Beyond itspioneering role as the first comprehensive solution for Story-to-Motion, oursystem undergoes evaluation across three distinct sub-tasks: trajectoryfollowing, temporal action composition, and motion blending, where itoutperforms previous state-of-the-art motion synthesis methods across theboard. Homepage: https://story2motion.github.io/.

Story-to-Motion: Synthesizing Infinite and Controllable Character Animation from Long Text