SEAL: 基于大规模贴纸-标签数据集的语义感知单图贴纸个性化
SEAL: Semantic-aware Single-image Sticker Personalization with a Large-scale Sticker-tag Dataset
AI总结：
SEAL通过引入语义感知的单图贴纸个性化方法，解决扩散模型在贴纸个性化中的视觉纠缠和结构刚性问题，利用StickerBench数据集实现属性级控制，提升身份保持与上下文可控性。
摘要：
在基于扩散模型的个性化文本到图像生成中，从单个参考图像合成目标概念具有挑战性，尤其是在贴纸个性化中，提示通常需要显式属性编辑。仅使用一个参考图像时，测试时间微调（TTF）方法容易过拟合，产生视觉纠缠，即背景伪影被吸收进学习的概念，以及结构刚性，即模型记忆参考特定的空间配置并失去上下文可控性。为了解决这些问题，我们引入了语义感知的单图贴纸个性化（SEAL），这是一个即插即用、架构无关的适应模块，可集成到现有个性化流程中而无需修改其基于U-Net的扩散主干。SEAL在嵌入适应过程中应用三个组件：（1）语义引导的空间注意力损失，（2）分割-合并令牌策略，以及（3）结构感知的层限制。为了支持贴纸领域的个性化并实现属性级控制，我们提出了StickerBench，一个大规模贴纸图像数据集，包含基于六属性方案（外观、情绪、动作、摄像构图、风格、背景）的结构化标签。这些注释提供了在变化上下文中保持目标身份一致的接口，从而能够系统评估身份解耦和上下文可控性。实验表明，SEAL在保持身份的同时维持上下文可控性，突显了在测试时间适应过程中显式空间和结构约束的重要性。代码、StickerBench和项目页面将公开发布。
英文摘要：
Synthesizing a target concept from a single reference image is challenging in diffusion-based personalized text-to-image generation, particularly for sticker personalization where prompts often require explicit attribute edits. With only one reference, test-time fine-tuning (TTF) methods tend to overfit, producing \textit{visual entanglement}, where background artifacts are absorbed into the learned concept, and \textit{structural rigidity}, where the model memorizes reference-specific spatial configurations and loses contextual controllability. To address these issues, we introduce \textbf{SE}mantic-aware single-image sticker person\textbf{AL}ization (\textbf{SEAL}), a plug-and-play, architecture-agnostic adaptation module that integrates into existing personalization pipelines without modifying their U-Net-based diffusion backbones. SEAL applies three components during embedding adaptation: (1) a Semantic-guided Spatial Attention Loss, (2) a Split-merge Token Strategy, and (3) Structure-aware Layer Restriction. To support sticker-domain personalization with attribute-level control, we present StickerBench, a large-scale sticker image dataset with structured tags under a six-attribute schema (Appearance, Emotion, Action, Camera Composition, Style, Background). These annotations provide a consistent interface for varying context while keeping target identity fixed, enabling systematic evaluation of identity disentanglement and contextual controllability. Experiments show that SEAL consistently improves identity preservation while maintaining contextual controllability, highlighting the importance of explicit spatial and structural constraints during test-time adaptation. The code, StickerBench, and project page will be publicly released.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
