Mohammad Babaeizadeh — accepted AI-conference papers

2023

Phenaki: Variable Length Video Generation from Open Domain Textual Descriptions

Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar +3

ICLR 2023poster

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 computational cost, limited quantities of high quality text-video data and variable length of videos. To address these issues, we i…

Cited by 440 Source ↗PDF ↗Scholar ↗

2023

StoryBench: A Multifaceted Benchmark for Continuous Story Visualization

Emanuele Bugliarello, Hernan Moraldo, Ruben Villegas, Mohammad Babaeizadeh, Mohammad Taghi Saffar, Han Zhang +4

NeurIPS 2023poster

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst being consistent throughout the frames. Creating a benchmark for video generation requires data annotated over time, which…

Cited by 8 Source ↗PDF ↗code ↗Scholar ↗

2022

Information Prioritization through Empowerment in Visual Model-based RL

Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine

ICLR 2022poster

Model-based reinforcement learning (RL) algorithms designed for handling complex visual observations typically learn some sort of latent state representation, either explicitly or implicitly. Standard methods of this sort do not distinguish between functionally relevant aspects of the state and irre…

Cited by 33 Source ↗PDF ↗Scholar ↗

2020

Adjustable Real-time Style Transfer

Mohammad Babaeizadeh, Golnaz Ghiasi

ICLR 2020poster

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 generate stylized images in real-time, these models produce a single stylization given a pair of style/content images,…

Cited by 28 Source ↗Scholar ↗

2020

Model Based Reinforcement Learning for Atari

Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H Campbell, Konrad Czechowski +8

ICLR 2020spotlight

Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How…

Cited by 1127 Source ↗code ↗Scholar ↗

2020

TRASS: Time Reversal as Self-Supervision

Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar

ICRA 2020poster

A longstanding challenge in robot learning for manipulation tasks has been the ability to generalize to varying initial conditions, diverse objects, and changing objectives. Learning based approaches have shown promise in producing robust policies, but require heavy supervision and large number of e…

Cited by 28 Source ↗Scholar ↗

2020

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh +1

ICLR 2020poster

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 central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events…

Cited by 123 Source ↗code ↗Scholar ↗

2018

Stochastic Variational Video Prediction

Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine

ICLR 2018poster

Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intrica…

Cited by 671 Source ↗PDF ↗Scholar ↗

2017

Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz

ICLR 2017poster

We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and concentrate on aspects critical to leveraging the GPU's computational po…

Cited by 385 Source ↗code ↗Scholar ↗