Recent advancements in visual content generation have made it easier than ever to generate remarkable imagery, often limited only by one’s imagination. However, unlike images, video generation requires both spatial and, critically, temporal understanding, posing unique and exciting challenges for existing models.
In this talk, I will explore key milestones in achieving coherent video generation through the lens of my works in the field. Each work tackles a different aspect of video generation, from temporal aliasing to video customization and motion comprehension. For each, I will first analyze prior approaches and identify key failure modes that lead to spatial or temporal incoherence. I will then present solutions based on the analyses to mitigate these issues—without requiring any additional data or model scaling. Finally, I will discuss open challenges and propose directions for future research.
Bio:
Hila is a PhD candidate at Tel Aviv University, advised by Prof. Lior Wolf. Her research focuses on understanding, interpreting, and correcting the predictions of deep foundational models. During her PhD, she interned at Google Research, Google DeepMind, and Meta AI, where she worked on video generation. Hila has received several awards, including the Fulbright Postdoctoral Fellowship, the Eric and Wendy Schmidt Postdoctoral Award, the Deutsch Prize for Outstanding PhD Students, and the Council for Higher Education (VATAT) Award for Outstanding PhD Students.
