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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present a method to animate a character incorporating multiple part-wisemotion priors (PMP). While previous works allow creating realistic articulatedmotions from reference data, the range of motion is largely limited by theavailable samples. Especially for the interaction-rich scenarios, it isimpractical to attempt acquiring every possible interacting motion, as thecombination of physical parameters increases exponentially. The proposed PMPallows us to assemble multiple part skills to animate a character, creating adiverse set of motions with different combinations of existing data. In ourpipeline, we can train an agent with a wide range of part-wise priors.Therefore, each body part can obtain a kinematic insight of the style from themotion captures, or at the same time extract dynamics-related information fromthe additional part-specific simulation. For example, we can first train ageneral interaction skill, e.g. grasping, only for the dexterous part, and thencombine the expert trajectories from the pre-trained agent with the kinematicpriors of other limbs. Eventually, our whole-body agent learns a novel physicalinteraction skill even with the absence of the object trajectories in thereference motion sequence.

PMP: Learning to Physically Interact with Environments using Part-wise Motion Priors