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20242026
most citedTuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer

2 citations · 2 across the 11 of their papers we have counts for

collaborators

14 papers

cs.CV2026

IntriAdapt: Generalizable Intrinsic-Guided Test-Time Adaptation for Metric Geometry Estimation

Jiaxing Chen, Zizheng Yan, Jingyu Zhuang +4

Recovering metric 3D geometry from images is fundamentally ill-posed due to perspective projection ambiguity. Current geometry models incorporate supplied or predicted intrinsics d…

cs.CV2026

WildSplat: Feedforward Gaussian Splatting from Unposed In-the-Wild Images

Xiyu Zhang, Jingyu Zhuang, Hongjia Zhai +4

While feedforward 3D reconstruction excels at efficient novel view synthesis, it typically falters when faced with scenes under varying illumination. To this end, we introduce Wild…

cs.CV2026

LiveMoments: Reselected Key Photo Restoration in Live Photos via Reference-guided Diffusion

Clara Xue, Zizheng Yan, Zhenning Shi +5

Live Photo captures both a high-quality key photo and a short video clip to preserve the precious dynamics around the captured moment. While users may choose alternative frames as…

cs.CV2026

One-Shot Refiner: Boosting Feed-forward Novel View Synthesis via One-Step Diffusion

Yitong Dong, Qi Zhang, Minchao Jiang +6

We present a novel framework for high-fidelity novel view synthesis (NVS) from sparse images, addressing key limitations in recent feed-forward 3D Gaussian Splatting (3DGS) methods…

cs.CV2025

Restore Text First, Enhance Image Later: Two-Stage Scene Text Image Super-Resolution with Glyph Structure Guidance

Minxing Luo, Linlong Fan, Wang Qiushi +7

Current image super-resolution methods show strong performance on natural images but distort text, creating a fundamental trade-off between image quality and textual readability. T…

cs.CV2025

Ultra-High-Definition Reference-Based Landmark Image Super-Resolution with Generative Diffusion Prior

Zhenning Shi, Zizheng Yan, Yuhang Yu +6

Reference-based Image Super-Resolution (RefSR) aims to restore a low-resolution (LR) image by utilizing the semantic and texture information from an additional reference high-resol…

cs.CV2025

GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors

Xingyilang Yin, Qi Zhang, Jiahao Chang +6

Reconstructing 3D scenes using 3D Gaussian Splatting (3DGS) from sparse views is an ill-posed problem due to insufficient information, often resulting in noticeable artifacts. Whil…

cs.CV2025

TinySR: Pruning Diffusion for Real-World Image Super-Resolution

Linwei Dong, Qingnan Fan, Yuhang Yu +4

Real-world image super-resolution (Real-ISR) focuses on recovering high-quality images from low-resolution inputs that suffer from complex degradations like noise, blur, and compre…

cs.CV2025

BokehDiff: Neural Lens Blur with One-Step Diffusion

Chengxuan Zhu, Qingnan Fan, Qi Zhang +4

We introduce BokehDiff, a novel lens blur rendering method that achieves physically accurate and visually appealing outcomes, with the help of generative diffusion prior. Previous…

cs.CV2025

Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders

Qiming Hu, Linlong Fan, Yiyan Luo +3

The introduction of generative models has significantly advanced image super-resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues,…

cs.CV2024

Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors

Jiangang Wang, Qingnan Fan, Qi Zhang +4

Owing to the robust priors of diffusion models, recent approaches have shown promise in addressing real-world super-resolution (Real-SR). However, achieving semantic consistency an…

cs.CV2024

RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-Resolution

Jiangang Wang, Qingnan Fan, Jinwei Chen +3

Benefiting from their powerful generative capabilities, pretrained diffusion models have garnered significant attention for real-world image super-resolution (Real-SR). Existing di…

cs.CV2024

TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution

Linwei Dong, Qingnan Fan, Yihong Guo +5

Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models,…

cs.CV2024★ 2 cited

Tuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer

Yanqi Ge, Jiaqi Liu, Qingnan Fan +6

In this work, we target the task of text-driven style transfer in the context of text-to-image (T2I) diffusion models. The main challenge is consistent structure preservation while…