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Single Image Video Prediction with Auto-Regressive GANs.

Jiahui Huang1, Yew Ken Chia2, Samson Yu2

  • 1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

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|May 20, 2022
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Summary
This summary is machine-generated.

This study presents a novel autoregressive method for generating future video frames from a single image. The approach enhances detail preservation and captures pixel changes, outperforming existing video prediction models.

Keywords:
autoregressive GANsemotion generationvideo prediction

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Predicting future video frames from static images is a challenging task.
  • Existing methods often suffer from detail loss and generation quality degradation.

Purpose of the Study:

  • To introduce a new autoregressive approach for future frame prediction from a single input image.
  • To improve the quality and detail preservation in generated video sequences.

Main Methods:

  • An autoregressive generation process where each time step's output feeds into the next.
  • Implementation of a "complementary mask" module to focus on pixel changes and reuse static information.

Main Results:

  • The method successfully generates high-quality, realistic video sequences from a single image.
  • Demonstrated superior performance compared to other video prediction models on the UT Dallas Dataset.
  • Validated robustness on the ADFES facial expression dataset and showed qualitative results on a human action dataset.

Conclusions:

  • The proposed method effectively generates detailed future frames by focusing on necessary pixel modifications.
  • This approach offers a significant improvement in video prediction quality and robustness.