80 citations · 120 across the 5 of their papers we have counts for
8 papers
Phenaki: Variable Length Video Generation From Open Domain Textual Description
Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans +6
We present Phenaki, a model capable of realistic video synthesis, given a sequence of textual prompts. Generating videos from text is particularly challenging due to the computatio…
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL
Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan +1
Model-based reinforcement learning (RL) algorithms designed for handling complex visual observations typically learn some sort of latent state representation, either explicitly or…
FitVid: Overfitting in Pixel-Level Video Prediction
Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair +3
An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally repr…
Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner +4
Model-based reinforcement learning (MBRL) methods have shown strong sample efficiency and performance across a variety of tasks, including when faced with high-dimensional visual o…
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan +4
Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. However, a…
Adjustable Real-time Style Transfer
Mohammad Babaeizadeh, Golnaz Ghiasi
Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can…