Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction

CVPR 2020 Vincent Le GuenNicolas Thome

Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods. Since physics is too restrictive for describing the full visual content of generic videos, we introduce PhyDNet, a two-branch deep architecture, which explicitly disentangles PDE dynamics from unknown complementary information... (read more)

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