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Qirui Yang

6 accepted papers

2026

Any-to-Bokeh: Arbitrary-Subject Video Refocusing with Video Diffusion Model

ICLR 2026poster

Diffusion models have recently emerged as powerful tools for camera simulation, enabling both geometric transformations and realistic optical effects. Among these, image-based bokeh rendering has shown promising results, but diffusion for video bokeh remains unexplored. Existing image-based methods…

Cited by 0SourcecodeScholar
2026

Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement

AAAI 2026technical

In image enhancement tasks, such as low-light and underwater image enhancement, a degraded image can correspond to multiple plausible target images due to dynamic photography conditions. This naturally results in a one-to-many mapping problem. To address this, we propose a Bayesian Enhancement Model

Cited by 0SourcePDFScholar
2026

FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration

CVPR 2026

All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-specific designs or latent routing strategies, making it hard to adapt to real-world scenarios with various degradations.

Cited by 0SourcecodeScholar
2025

Learning Adaptive Lighting via Channel-Aware Guidance

ICML 2025poster

Learning lighting adaptation is a crucial step in achieving good visual perception and supporting downstream vision tasks. Current research often addresses individual light-related challenges, such as high dynamic range imaging and exposure correction, in isolation. However, we identify shared funda…

2025

Learning Differential Pyramid Representation for Tone Mapping

NeurIPS 2025poster

Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. These designs typically fail to preserve fin…

Cited by 0SourcecodeScholar
2024

Penetrative AI: Making LLMs Comprehend the Physical World

ACL 2024findings

Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks. Questions, however, persist about the nature of LLMs and their potential to integrate common-sense human knowledge when performing tasks involving information about the real p…