collaborators

20 papers

cs.CV2026

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…

cs.CV2026

GS-STVSR: Ultra-Efficient Continuous Spatio-Temporal Video Super-Resolution via 2D Gaussian Splatting

Mingyu Shi, Xin Di, Long Peng +8

Continuous Spatio-Temporal Video Super-Resolution (C-STVSR) aims to simultaneously enhance the spatial resolution and frame rate of videos by arbitrary scale factors, offering grea…

cs.CV2026

The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

Jiatong Li, Zheng Chen, Kai Liu +91

This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…