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arXiv · 1712.01955

Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering

Mengyao Zhai·Jiacheng Chen·Ruizhi Deng·Lei Chen·Ligeng Zhu·Greg Mori
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Abstract

We propose an approach for forecasting video of complex human activity involving multiple people. Direct pixel-level prediction is too simple to handle the appearance variability in complex activities. Hence, we develop novel intermediate representations. An architecture combining a hierarchical temporal model for predicting human poses and encoder-decoder convolutional neural networks for rendering target appearances is proposed. Our hierarchical model captures interactions among people by adopting a dynamic group-based interaction mechanism. Next, our appearance rendering network encodes the targets' appearances by learning adaptive appearance filters using a fully convolutional network. Finally, these filters are placed in encoder-decoder neural networks to complete the rendering. We demonstrate that our model can generate videos that are superior to state-of-the-art methods, and can handle complex human activity scenarios in video forecasting.

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Mengyao Zhai, Jiacheng Chen, Ruizhi Deng, Lei Chen, Ligeng Zhu, Greg Mori. 2017-12-05. Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering. https://arxiv.org/abs/1712.01955

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