activity
20182026
most citedModel-Based Imitation Learning for Urban Driving

33 citations · 81 across the 6 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Multiplayer Interactive World Models with Representation Autoencoders

Anthony Hu, Václav Volhejn, Adrien Ramanana Rahary +24

We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interactions. Whereas single-player world models treat the other agents…

cs.CV202327 cited

GAIA-1: A Generative World Model for Autonomous Driving

Anthony Hu, Lloyd Russell, Hudson Yeo +5

Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios rem…

cs.CV20233 cited

Neural World Models for Computer Vision

Anthony Hu

Humans navigate in their environment by learning a mental model of the world through passive observation and active interaction. Their world model allows them to anticipate what mi…

cs.CV202233 cited

Model-Based Imitation Learning for Urban Driving

Anthony Hu, Gianluca Corrado, Nicolas Griffiths +6

An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning app…

cs.CV2021

FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras

Anthony Hu, Zak Murez, Nikhil Mohan +5

Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's…

cs.CV2020

Probabilistic Future Prediction for Video Scene Understanding

Anthony Hu, Fergal Cotter, Nikhil Mohan +2

We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scene…