Stochastic Latent Residual Video Prediction

Jean-Yves Franceschi (1) , Edouard Delasalles (1) , Mickaël Chen (1) , Sylvain Lamprier (1) , Patrick Gallinari (1, 2)

1 MLIA - Machine Learning and Information Access
2 Criteo AI Lab

Designing video prediction models that account for the inherent uncertainty of the future is challenging. Most works in the literature are based on stochastic image-autoregressive recurrent networks, which raises several performance and applicability issues. An alternative is to use fully latent temporal models which untie frame synthesis and temporal dynamics. However, no such model for stochastic video prediction has been proposed in the literature yet, due to design and training difficulties. In this paper, we overcome these difficulties by introducing a novel stochastic temporal model whose dynamics are governed in a latent space by a residual update rule. This first-order scheme is motivated by discretization schemes of differential equations. It naturally models video dynamics as it allows our simpler, more interpretable, latent model to outperform prior state-of-the-art methods on challenging datasets.

Mots clés: Stochastic Video Prediction; Generative Models; Variational Autoencoders; State-Space Models; ICML; Deep Learning; Machine Learning

Domaines: Apprentissage [cs.LG]; Machine Learning [stat.ML]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari. Stochastic Latent Residual Video Prediction. Thirty-seventh International Conference on Machine Learning, International Machine Learning Society, Jul 2020, Vienne, Austria. pp.3233--3246. ⟨hal-02484182v4⟩