多模态大模型中ID一致性优化：通过对齐、纠缠与解纠缠进行面部修复
Optimizing ID Consistency in Multimodal Large Models: Facial Restoration via Alignment, Entanglement, and Disentanglement
AI总结：
EditedID通过引入对齐、解纠缠和纠缠机制，提升多模态大模型中面部身份一致性的修复能力。
摘要：
多模态编辑大模型在各种任务中展示了强大的编辑能力。然而，一个持续且长期存在的限制是，在真实肖像编辑中面部身份（ID）一致性下降。由于人眼对面部特征的高度敏感，这种不一致性显著阻碍了这些模型的实际部署。当前的面部ID保存方法由于跨源分布偏见和跨源特征污染，难以实现面部身份和编辑元素IP的一致性恢复。为了解决这些问题，我们提出了EditedID，一种用于稳健身份特定面部修复的对齐-解纠缠-纠缠框架。通过系统分析扩散轨迹、采样行为和注意力属性，我们引入了三个关键组件：1）自适应混合策略，通过扩散过程对齐跨源潜在表示。2）混合求解器，解纠缠源特定身份属性和细节。3）注意力门控机制，选择性地纠缠视觉元素。广泛的实验表明，EditedID在保持原始面部ID和编辑元素IP一致性方面实现了最先进的性能。作为一种无需训练且即插即用的解决方案，它为开放世界中单人或多个人面部身份修复建立了新的基准，为多模态编辑大模型在真实人物编辑场景中的部署铺平了道路。代码可在https://github.com/NDYBSNDY/EditedID上获得。
英文摘要：
Multimodal editing large models have demonstrated powerful editing capabilities across diverse tasks. However, a persistent and long-standing limitation is the decline in facial identity (ID) consistency during realistic portrait editing. Due to the human eye's high sensitivity to facial features, such inconsistency significantly hinders the practical deployment of these models. Current facial ID preservation methods struggle to achieve consistent restoration of both facial identity and edited element IP due to Cross-source Distribution Bias and Cross-source Feature Contamination. To address these issues, we propose EditedID, an Alignment-Disentanglement-Entanglement framework for robust identity-specific facial restoration. By systematically analyzing diffusion trajectories, sampler behaviors, and attention properties, we introduce three key components: 1) Adaptive mixing strategy that aligns cross-source latent representations throughout the diffusion process. 2) Hybrid solver that disentangles source-specific identity attributes and details. 3) Attentional gating mechanism that selectively entangles visual elements. Extensive experiments show that EditedID achieves state-of-the-art performance in preserving original facial ID and edited element IP consistency. As a training-free and plug-and-play solution, it establishes a new benchmark for practical and reliable single/multi-person facial identity restoration in open-world settings, paving the way for the deployment of multimodal editing large models in real-person editing scenarios. The code is available at https://github.com/NDYBSNDY/EditedID.
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。
