activity
20242026
collaborators

5 papers

cs.CV2026

VOID: Video Object and Interaction Deletion

Saman Motamed, William Harvey, Benjamin Klein +3

Existing video object removal methods excel at inpainting content "behind" the object and correcting appearance-level artifacts such as shadows and reflections. However, when the r…

cs.CV2026

InTraGen: Trajectory-controlled Video Generation for Object Interactions

Zuhao Liu, Aleksandar Yanev, Ahmad Mahmood +7

Advances in video generation have significantly improved the realism and quality of created scenes. This has fueled interest in developing intuitive tools that let users leverage v…

cs.CV2025

TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibility

Saman Motamed, Minghao Chen, Luc Van Gool +1

Despite impressive visual fidelity, modern video generative models frequently produce sequences that violate intuitive physical laws, such as objects floating, teleporting, or morp…

cs.CV2025

Do generative video models understand physical principles?

Saman Motamed, Laura Culp, Kevin Swersky +2

AI video generation is undergoing a revolution, with quality and realism advancing rapidly. These advances have led to a passionate scientific debate: Do video models learn "world…

cs.CV2024

Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models

Saman Motamed, Danda Pani Paudel, Luc Van Gool

Text-to-Image (T2I) models excel at synthesizing concepts such as nouns, appearances, and styles. To enable customized content creation based on a few example images of a concept,…