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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

Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset

Senyan Xu, Zhijing Sun, Kean Liu +5

Event-based low-light image enhancement (LIE) methods mainly focus on incorporating high dynamic range (HDR) information from events while overlooking the essential global illumina…

cs.CV2026

EventGait: Towards Robust Gait Recognition with Event Streams

Senyan Xu, Shuai Chen, Chuanfu Shen +4

Gait recognition enables non-intrusive, privacy-preserving identification but suffers in uncontrolled environments due to illumination and motion sensitivity of conventional camera…

cs.CV2026

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…

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…