Skeleton-based human motion prediction via spatio and position encoding transformer network

Lingchao Mi; Rui Ding; Xue Zhang

doi:10.1117/12.2667366

1 March 2023 Skeleton-based human motion prediction via spatio and position encoding transformer network

Lingchao Mi, Rui Ding, Xue Zhang

Proceedings Volume 12588, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022); 125880Q (2023) https://doi.org/10.1117/12.2667366
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 2022, Chongqing, China

Abstract

Many transformer modules, have been applied to computer vision. However, the transformer can extract the distal connections of human skeleton points and apply the attention mechanism to the problem of predicting human motion pose. We introduce a transformer module in the joint dimension. In addition, the Encoder module of the transformer is improved. Finally, our method achieves impressive results on benchmark datasets, including short- and long-term predictions of FNTU, confirming its effectiveness and efficiency.

Citation Download Citation

Lingchao Mi, Rui Ding, and Xue Zhang "Skeleton-based human motion prediction via spatio and position encoding transformer network", Proc. SPIE 12588, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 125880Q (1 March 2023); https://doi.org/10.1117/12.2667366

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available