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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Veröffentlicht in:	Proceedings / IEEE Workshop on Applications of Computer Vision S. 2753 - 2762
Hauptverfasser:	Degardin, Bruno, Neves, Joao, Lopes, Vasco, Brito, Joao, Yaghoubi, Ehsan, Proenca, Hugo
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.01.2022
Schlagworte:	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-Zugang:	Volltext
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