← 研踪 PaperTrailCVF · CVPR 2026:cvf:162a2c5e4014700c626c38d948cdbecb · 记录 #1
Style-GRPO: Semantic-Aware Preference Optimization for Image Style Transfer Guided by Reward Modeling
Zhao, JianbinFeng, ChaoranYu, MiaoLi, YingtaoTang, ZhenyuYu, WangboZhao, YianLi, XiaominYuan, LiTian, Yonghong 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:01
摘要
Recent progress in text-to-image generation has greatly advanced visual fidelity and creativity, but it has also imposed higher demands on prompt complexity--particularly in encoding intricate spatial relationships. In such cases, achieving satisfactory results often requires multiple sampling attempts.To address this challenge, we introduce a novel method that strengthens the spatial understanding of current image generation models. We first construct the **SpatialReward-Dataset** with over 80k preference pairs. Building on this dataset, we build **SpatialScore**, a reward model designed to evaluate the accuracy of spatial relationships in text-to-image generation, achieving performance that even surpasses leading proprietary models on spatial evaluation.% Through rigorous data curation and filtering, **SpatialScore** surpasses several leading vision-language models (VLMs) and existing reward models on spatial evaluation. We further demonstrate that this reward model effectively enables online reinforcement learning for the complex spatial generation. Extensive experiments across multiple benchmarks show that our specialized reward model yields significant and consistent gains in spatial understanding for image generation. All models and datasets will be released.
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记录 #1 · 2026 · 最新观测版本
Style-GRPO: Semantic-Aware Preference Optimization for Image Style Transfer Guided by Reward Modeling
Recent progress in text-to-image generation has greatly advanced visual fidelity and creativity, but it has also imposed higher demands on prompt complexity--particularly in encoding intricate spatial relationships. In such cases, achieving satisfactory results often requires multiple sampling attempts.To address this challenge, we introduce a novel method that strengthens the spatial understanding of current image generation models. We first construct the **SpatialReward-Dataset** with over 80k preference pairs. Building on this dataset, we build **SpatialScore**, a reward model designed to evaluate the accuracy of spatial relationships in text-to-image generation, achieving performance that even surpasses leading proprietary models on spatial evaluation.% Through rigorous data curation and filtering, **SpatialScore** surpasses several leading vision-language models (VLMs) and existing reward models on spatial evaluation. We further demonstrate that this reward model effectively enables online reinforcement learning for the complex spatial generation. Extensive experiments across multiple benchmarks show that our specialized reward model yields significant and consistent gains in spatial understanding for image generation. All models and datasets will be released.
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