Nicolas Urbani’s personal page

Nicolas Urbani

Nicolas Urbani


Intern March-August 2021

Research topics with IPAL

Future Video Prediction using Generative Models

Learning to predict the future is an important research problem in machine learning and artificial intelligence. In this project, we focus on the task of predicting future frames in videos, i.e., video prediction, given a sequence of previous frames. Recently, deep-learning-based methods have emerged as a promising approach for video prediction, especially generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs). VAEs can generate various plausible outcomes, however, the predicted frames are blurry and of low quality. While GAN-based models tend to produce higher quality future frames, adversarial training is unstable and may lead to model collapse. Therefore, we will explore state-of-the-art generative models for video prediction and develop new strategies to address the limitations of existing methods.

Collaborators at IPAL

  • Christophe Jouffrais
  • Ying Sun

Link to webpage

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