Learning Latent Global Network for Skeleton-Based Action Prediction

Volume: 29, Pages: 959 - 970
Published: Jan 1, 2020
Abstract
Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes. In this paper, we investigate skeleton-based action prediction, which aims to recognize an action from a partial skeleton sequence that contains incomplete action information. We propose a new Latent Global Network based on adversarial learning for action prediction. We demonstrate that the proposed network provides latent long-term...
Paper Details
Title
Learning Latent Global Network for Skeleton-Based Action Prediction
Published Date
Jan 1, 2020
Volume
29
Pages
959 - 970
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