10 papers
From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation
Xingjian Wang, Zhao Wang, Taihang Hu +17
Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize task-specific datasets…
Exploring the Performance Frontier of Compact Unified Image Generation Models
Taihang Hu, Zhao Wang, Zuan Gao +20
We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing. Our goal is to explore how far a relatively small visua…
CPI-Bench: A Comprehensive, Practical and Intelligent Benchmark for Real-World Image Editing
Qinye Zhou, Jun Zheng, Yongchao Du +17
With the rapid advancement of image editing models and their widespread application across various domains, there is an increasingly urgent need to deploy these model capabilities…
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…
SemiNFT: Learning to Transfer Presets from Imitation to Appreciation via Hybrid-Sample Reinforcement Learning
Melany Yang, Yuhang Yu, Diwang Weng +2
Photorealistic color retouching plays a vital role in visual content creation, yet manual retouching remains inaccessible to non-experts due to its reliance on specialized expertis…
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…
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…
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…
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,…
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…