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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 10/2023

18.06.2023 | Manuscript

Conditional Temporal Variational AutoEncoder for Action Video Prediction

verfasst von: Xiaogang Xu, Yi Wang, Liwei Wang, Bei Yu, Jiaya Jia

Erschienen in: International Journal of Computer Vision | Ausgabe 10/2023

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Abstract

To synthesize a realistic action sequence based on a single human image, it is crucial to model both motion patterns and diversity in the action video. This paper proposes an Action Conditional Temporal Variational AutoEncoder (ACT-VAE) to improve motion prediction accuracy and capture movement diversity. ACT-VAE predicts pose sequences for an action clip from a single input image. It is implemented as a deep generative model that maintains temporal coherence according to the action category with a novel temporal modeling on latent space. Further, ACT-VAE is a general action sequence prediction framework. When connected with a plug-and-play Pose-to-Image network, ACT-VAE can synthesize image sequences. Extensive experiments bear out our approach can predict accurate pose and synthesize realistic image sequences, surpassing state-of-the-art approaches. Compared to existing methods, ACT-VAE improves model accuracy and preserves diversity.
Vorheriger Artikel Anti-Bandit for Neural Architecture Search
Nächster Artikel Visually-Guided Audio Spatialization in Video with Geometry-Aware Multi-task Learning

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Metadaten
Titel
Conditional Temporal Variational AutoEncoder for Action Video Prediction
verfasst von
Xiaogang Xu
Yi Wang
Liwei Wang
Bei Yu
Jiaya Jia
Publikationsdatum
18.06.2023
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 10/2023
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-023-01832-8

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