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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.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.CV2025

MonSTeR: a Unified Model for Motion, Scene, Text Retrieval

Luca Collorone, Matteo Gioia, Massimiliano Pappa +5

Intention drives human movement in complex environments, but such movement can only happen if the surrounding context supports it. Despite the intuitive nature of this mechanism, e…

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

OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras

Muhammad Rameez Ur Rahman, Jhony H. Giraldo, Indro Spinelli +2

Event cameras, known for low-latency operation and superior performance in challenging lighting conditions, are suitable for sensitive computer vision tasks such as semantic segmen…

cs.CV2024

Length-Aware Motion Synthesis via Latent Diffusion

Alessio Sampieri, Alessio Palma, Indro Spinelli +1

The target duration of a synthesized human motion is a critical attribute that requires modeling control over the motion dynamics and style. Speeding up an action performance is no…