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cs.CV2026

Video Unlearning via Low-Rank Refusal Vector

Simone Facchiano, Stefano Saravalle, Matteo Migliarini +7

Video generative models achieve high-quality synthesis from natural-language prompts by leveraging large-scale web data. However, this training paradigm inherently exposes them to…

cs.CV2026

TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos

Leonardo Plini, Luca Scofano, Edoardo De Matteis +6

Identifying procedural errors online from egocentric videos is a critical yet challenging task across various domains, including manufacturing, healthcare, and skill-based training…

cs.CV2025

Human Motion Unlearning

Edoardo De Matteis, Matteo Migliarini, Alessio Sampieri +2

We introduce Human Motion Unlearning and motivate it through the concrete task of preventing violent 3D motion synthesis, an important safety requirement given that popular text-to…

cs.CV2024

Social EgoMesh Estimation

Luca Scofano, Alessio Sampieri, Edoardo De Matteis +2

Accurately estimating the 3D pose of the camera wearer in egocentric video sequences is crucial to modeling human behavior in virtual and augmented reality applications. The task p…

cs.CV2024

PREGO: online mistake detection in PRocedural EGOcentric videos

Alessandro Flaborea, Guido Maria D'Amely di Melendugno, Leonardo Plini +5

Promptly identifying procedural errors from egocentric videos in an online setting is highly challenging and valuable for detecting mistakes as soon as they happen. This capability…