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cs.CV2025

QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-Design

Benjamin Schneider, Dongfu Jiang, Chao Du +2

Long-video understanding has emerged as a crucial capability in real-world applications such as video surveillance, meeting summarization, educational lecture analysis, and sports…

cs.CV2025

Error Analyses of Auto-Regressive Video Diffusion Models: A Unified Framework

Jing Wang, Fengzhuo Zhang, Xiaoli Li +5

Auto-Regressive Video Diffusion Models (AR-VDMs) have shown strong capabilities in generating long, photorealistic videos, but suffer from two key limitations: (i) history forgetti…

cs.CV2025

Real-time Identity Defenses against Malicious Personalization of Diffusion Models

Hanzhong Guo, Shen Nie, Chao Du +3

Personalized generative diffusion models, capable of synthesizing highly realistic images based on a few reference portraits, may pose substantial social, ethical, and legal risks…

cs.CV2024

Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models

Zehan Wang, Ziang Zhang, Tianyu Pang +3

Orientation is a key attribute of objects, crucial for understanding their spatial pose and arrangement in images. However, practical solutions for accurate orientation estimation…

cs.CV2024

Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts

Hongcheng Gao, Tianyu Pang, Chao Du +3

With the rapid progress of diffusion-based content generation, significant efforts are being made to unlearn harmful or copyrighted concepts from pretrained diffusion models (DMs)…

cs.CV2024

Improving Long-Text Alignment for Text-to-Image Diffusion Models

Luping Liu, Chao Du, Tianyu Pang +3

The rapid advancement of text-to-image (T2I) diffusion models has enabled them to generate unprecedented results from given texts. However, as text inputs become longer, existing e…