通过解耦身份与属性特征，实现无需训练的精准人脸编辑
Zero-shot Face Editing via ID-Attribute Decoupled Inversion
- 将人脸特征拆分为身份和属性两部分，联合指导图像重建与生成
- 仅用文本提示即可完成复杂多属性编辑，保持身份一致性
- 适用于无标注数据的零样本人脸编辑，速度接近传统反向扩散方法
近期基于文本引导的扩散模型在图像编辑中展现出潜力，但实际人脸编辑中常难以维持身份与结构一致性。为此，我们提出一种基于身份-属性解耦反演的零样本人脸编辑方法。具体而言，将人脸表示分解为身份特征与属性特征，将其作为联合条件，引导反演与逆向扩散过程。该方法可独立控制身份与属性，确保身份强保留与结构一致，同时实现精确的人脸属性调整。本方法仅需文本提示即可支持多种复杂多属性人脸编辑任务，无需区域指定输入，且运行速度接近DDIM反演。大量实验验证了其实用性和有效性。
原文摘要 · Abstract (English)
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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