Generative Adversarial Graph Convolutional Networks for Human Action Synthesis

Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning). In this paper,...

Full description

Bibliographic Details

Published in:	Proceedings / IEEE Workshop on Applications of Computer Vision pp. 2753 - 2762
Main Authors:	Degardin, Bruno, Neves, Joao, Lopes, Vasco, Brito, Joao, Yaghoubi, Ehsan, Proenca, Hugo
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.01.2022
Subjects:	Computer architecture Computer vision Convolutional codes Deep Learning -> Graph Neural Networks Biometrics -> Human Motion Analysis/Capture; Deep Learning; Deep Learning -> Adversarial Learning; Adversarial Attack and Defense Methods; Deep Learning -> Neural Generative Models; Autoencoders; GANs Generative adversarial networks Measurement Shape Stochastic processes
ISSN:	2642-9381
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!