Over the last few years, the research community has continuously pushed the boundary of
video generative modelling with many impressive demos of open and closed source models.
This led to an increasing interest in the steerability of such models and their ability to capture
the different dynamics present in the data. In this talk, we will discuss the recent advances in
world modelling applied to video games as an interesting setting for training such models. We
will discuss the recently published World and Human Action Model (WHAM) through the lens of
its design, its evaluation and key learning that came of scaling world models to a modern video
game title.
Bio
Abdelhak Lemkhenter is a Researcher at Microsoft Research Cambridge currently working on few-
shot imitation learning and world modeling in complex modern video games. His research
interests also include robust and scalable representation learning and data-centric
learning. He completed his PhD in Informatics at the University of Bern and obtained his Master
degree from the Ecole Central de Paris.