CraftGraffiti：通过面部保留扩散模型探索人类身份的定制涂鸦艺术
CraftGraffiti: Exploring Human Identity with Custom Graffiti Art via Facial-Preserving Diffusion Models
AI总结：
CraftGraffiti通过面部保留扩散模型生成具有身份一致性的涂鸦艺术，结合风格迁移与面部一致性机制，提升艺术创作的可识别性与审美价值。
摘要：
在生成艺术中，保持面部身份在极端风格转换下的挑战依然重大。在涂鸦这种高对比、抽象媒介中，细微的面部特征扭曲可能消除主体的可识别性，影响个人和文化真实性。我们提出了CraftGraffiti，一种端到端的文本引导涂鸦生成框架，以面部特征保留为主要目标。给定输入图像和风格与姿态描述提示，CraftGraffiti首先通过LoRA微调的预训练扩散变压器应用涂鸦风格迁移，然后通过面部一致的自注意力机制强制身份一致性，该机制通过显式身份嵌入增强注意力层。姿态定制无需关键点，使用CLIP引导的提示扩展实现动态重新定位，同时保持面部一致性。我们正式论证并实证了“先风格后身份”的范式，证明其比反向顺序减少属性漂移。定量结果展示了竞争性的面部特征一致性以及最先进的审美和人类偏好分数，而定性分析和Cruilla节现场部署突显了系统的真实世界创意影响。CraftGraffiti推进了尊重身份的AI辅助艺术目标，为在创意AI应用中融合风格自由与可识别性提供了系统性方法。
英文摘要：
Preserving facial identity under extreme stylistic transformation remains a major challenge in generative art. In graffiti, a high-contrast, abstract medium, subtle distortions to the eyes, nose, or mouth can erase the subject's recognizability, undermining both personal and cultural authenticity. We present CraftGraffiti, an end-to-end text-guided graffiti generation framework designed with facial feature preservation as a primary objective. Given an input image and a style and pose descriptive prompt, CraftGraffiti first applies graffiti style transfer via LoRA-fine-tuned pretrained diffusion transformer, then enforces identity fidelity through a face-consistent self-attention mechanism that augments attention layers with explicit identity embeddings. Pose customization is achieved without keypoints, using CLIP-guided prompt extension to enable dynamic re-posing while retaining facial coherence. We formally justify and empirically validate the "style-first, identity-after" paradigm, showing it reduces attribute drift compared to the reverse order. Quantitative results demonstrate competitive facial feature consistency and state-of-the-art aesthetic and human preference scores, while qualitative analyses and a live deployment at the Cruilla Festival highlight the system's real-world creative impact. CraftGraffiti advances the goal of identity-respectful AI-assisted artistry, offering a principled approach for blending stylistic freedom with recognizability in creative AI applications.
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。
