基于ID-属性解耦反演的零样本人脸编辑
Zero-shot Face Editing via ID-Attribute Decoupled Inversion
AI总结：
提出基于ID-属性解耦反演的零样本人脸编辑方法，通过分解人脸表示为ID和属性特征作为联合条件，实现独立控制与精确编辑，在保持ID和结构一致性的同时支持复杂多属性编辑。
摘要：
近期文本引导扩散模型的进展在通过反演技术进行通用图像编辑方面展现出潜力，但在真实人脸编辑任务中往往难以保持ID和结构一致性。为解决此局限，我们提出一种基于ID-属性解耦反演的零样本人脸编辑方法。具体而言，我们将人脸表示分解为ID和属性特征，将其作为联合条件引导反演和反向扩散过程。这实现了对ID和属性的独立控制，确保了强ID保留和结构一致性，同时实现了精确的面部属性操作。我们的方法仅需文本提示即可支持广泛的复杂多属性人脸编辑任务，无需特定区域输入，且运行速度与DDIM反演相当。综合实验证明了其实用性和有效性。
英文摘要：
Recent advancements in text-guided diffusion models have shown promise for general image editing via inversion techniques, but often struggle to maintain ID and structural consistency in real face editing tasks. To address this limitation, we propose a zero-shot face editing method based on ID-Attribute Decoupled Inversion. Specifically, we decompose the face representation into ID and attribute features, using them as joint conditions to guide both the inversion and the reverse diffusion processes. This allows independent control over ID and attributes, ensuring strong ID preservation and structural consistency while enabling precise facial attribute manipulation. Our method supports a wide range of complex multi-attribute face editing tasks using only text prompts, without requiring region-specific input, and operates at a speed comparable to DDIM inversion. Comprehensive experiments demonstrate its practicality and effectiveness.
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