8 papers
OmniVR: Joint Video-Audio Conditional Generation for Restoring Degraded Historical Films
Xin Lu, Zihao Fan, Mingchen Zhong +3
Historical films suffer from co-occurring visual and audio degradations---blur, noise, flicker, hiss, clipping, and dropout---yet existing methods restore each modality independent…
SketchAssist: A Practical Assistant for Semantic Edits and Precise Local Redrawing
Han Zou, Yan Zhang, Ruiqi Yu +3
Sketch editing requires jointly handling high-level semantic changes and precise local redrawing, a combination that is particularly challenging for sparse, style-sensitive line ar…
IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow
Zihao Fan, Xin Lu, Jie Xiao +3
In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from inefficient multi-step sampling…
Bird-SR: Bidirectional Reward-Guided Diffusion for Real-World Image Super-Resolution
Zihao Fan, Xin Lu, Yidi Liu +4
Powered by multimodal text-to-image priors, diffusion-based super-resolution excels at synthesizing intricate details; however, models trained on synthetic low-resolution (LR) and…
Iterative Inference-time Scaling with Adaptive Frequency Steering for Image Super-Resolution
Hexin Zhang, Dong Li, Jie Huang +3
Diffusion models have become a leading paradigm for image super-resolution (SR), but existing methods struggle to guarantee both the high-frequency perceptual quality and the low-f…
FinPercep-RM: A Fine-grained Reward Model and Co-evolutionary Curriculum for RL-based Real-world Super-Resolution
Yidi Liu, Zihao Fan, Jie Huang +6
Reinforcement Learning with Human Feedback (RLHF) has proven effective in image generation field guided by reward models to align human preferences. Motivated by this, adapting RLH…