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cs.CV2026

Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation

Niantong Li, Guangzheng Hu, Weixu Qiao +35

Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no…

cs.CV2026

Pareto-Guided Optimal Transport for Multi-Reward Alignment

Ying Ba, Tianyu Zhang, Mohan Zhou +5

Text-to-image generation models have achieved remarkable progress in preference optimization, yet achieving robust alignment across diverse reward models remains a significant chal…

cs.CV2026

SyncDPO: Enhancing Temporal Synchronization in Video-Audio Joint Generation via Preference Learning

Xin Cheng, Xihua Wang, Ying Ba +5

Recent advancements in video-audio joint generation have achieved remarkable success in semantic correspondence. However, achieving precise temporal synchronization, which requires…

cs.CV2026

Qwen-Image-2.0 Technical Report

Bing Zhao, Chenfei Wu, Deqing Li +72

We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite rece…

cs.CV2026

Say Cheese! Detail-Preserving Portrait Collection Generation via Natural Language Edits

Zelong Sun, Jiahui Wu, Ying Ba +2

As social media platforms proliferate, users increasingly demand intuitive ways to create diverse, high-quality portrait collections. In this work, we introduce Portrait Collection…

cs.CV2025

PrefGen: Multimodal Preference Learning for Preference-Conditioned Image Generation

Wenyi Mo, Tianyu Zhang, Yalong Bai +3

Preference-conditioned image generation seeks to adapt generative models to individual users, producing outputs that reflect personal aesthetic choices beyond the given textual pro…