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,...

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Vydáno v:	Proceedings / IEEE Workshop on Applications of Computer Vision s. 2753 - 2762
Hlavní autoři:	Degardin, Bruno, Neves, Joao, Lopes, Vasco, Brito, Joao, Yaghoubi, Ehsan, Proenca, Hugo
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.01.2022
Témata:	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
On-line přístup:	Získat plný text
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