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

The most popular type of devices used to track a user's posture in a virtualreality experience consists of a head-mounted display and two controllers heldin both hands. However, due to the limited number of tracking sensors (three intotal), faithfully recovering the user in full-body is challenging, limitingthe potential for interactions among simulated user avatars within the virtualworld. Therefore, recent studies have attempted to reconstruct full-body posesusing neural networks that utilize previously learned human poses or accept aseries of past poses over a short period. In this paper, we propose a methodthat utilizes information from a neural motion prior to improve the accuracy ofreconstructed user's motions. Our approach aims to reconstruct user's full-bodyposes by predicting the latent representation of the user's overall motion fromlimited input signals and integrating this information with tracking sensorinputs. This is based on the premise that the ultimate goal of posereconstruction is to reconstruct the motion, which is a series of poses. Ourresults show that this integration enables more accurate reconstruction of theuser's full-body motion, particularly enhancing the robustness of lower bodymotion reconstruction from impoverished signals. Web:https://https://mjsh34.github.io/mp-sspe/

Utilizing Task-Generic Motion Prior to Recover Full-Body Motion from Very Sparse Signals