7 papers · 1 filter
LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models
Lu Liu, Huiyu Duan, Chenxin Zhu +6
Large-scale generative models have demonstrated remarkable capabilities across image generation and editing tasks. However, their performance in low-level vision tasks, which requi…
A2BFR: Attribute-Aware Blind Face Restoration
Chenxin Zhu, Yushun Fang, Lu Liu +5
Blind face restoration (BFR) aims to recover high-quality facial images from degraded inputs, yet its inherently ill-posed nature leads to ambiguous and uncontrollable solutions. R…
MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration
Lu Liu, Chunlei Cai, Shaocheng Shen +9
Real-world videos often suffer from complex degradations, such as noise, compression artifacts, and low-light distortions, due to diverse acquisition and transmission conditions. E…
F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration
Lu Liu, Huiyu Duan, Qiang Hu +6
Artificial intelligence generative models exhibit remarkable capabilities in content creation, particularly in face image generation, customization, and restoration. However, curre…
Robust ID-Specific Face Restoration via Alignment Learning
Yushun Fang, Lu Liu, Xiang Gao +5
The latest developments in Face Restoration have yielded significant advancements in visual quality through the utilization of diverse diffusion priors. Nevertheless, the uncertain…
Omni: Unifying Omnidirectional Image Generation and Editing in an Omni Model
Liu Yang, Huiyu Duan, Yucheng Zhu +7
omnidirectional images (ODIs) have gained considerable attention recently, and are widely used in various virtual reality (VR) and augmented reality (AR) applications…