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Conditional Temporal Variational AutoEncoder for Action Video Prediction

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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.

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Data Availability

The data that support the results and analysis of this study is publicly available in a repository. The dataset of Penn-action is available at http://dreamdragon.github.io/PennAction. The dataset of Human3.6M is available at http://vision.imar.ro/human3.6m/description.php. The dataset of NTU RGB+D Dataset is available at https://rose1.ntu.edu.sg/dataset/actionRecognition.

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Acknowledgements

This work is supported by Key Research Project of Zhejiang Lab (No. K2022PG1BB01). This work is also supported by Research Project of Zhejiang Lab (No.2022PD0AC02).

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  1. Zhejiang Lab, Hangzhou City, Zhejiang Province, China

    Xiaogang Xu

  2. Shanghai AI Laboratory, Shanghai City, China

    Yi Wang

  3. The Chinese University of Hong Kong, Hong Kong, China

    Liwei Wang, Bei Yu & Jiaya Jia

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Xu, X., Wang, Y., Wang, L. et al. Conditional Temporal Variational AutoEncoder for Action Video Prediction. Int J Comput Vis 131, 2699–2722 (2023). https://doi.org/10.1007/s11263-023-01832-8

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