most citedGlobal-local Motion Transformer for Unsupervised Skeleton-based Action Learning

3 citations · 3 across the 2 of their papers we have counts for

collaborators
Showing cs.CVShow all

2 papers · 1 filter

cs.CV2022

R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement

Sehwan Choi, Jungho Kim, Junyong Yun +1

Predicting the future motion of dynamic agents is of paramount importance to ensuring safety and assessing risks in motion planning for autonomous robots. In this study, we propose…

cs.CV2022★ 3 cited

Global-local Motion Transformer for Unsupervised Skeleton-based Action Learning

Boeun Kim, Hyung Jin Chang, Jungho Kim +1

We propose a new transformer model for the task of unsupervised learning of skeleton motion sequences. The existing transformer model utilized for unsupervised skeleton-based actio…