连接隐私与溯源：可追溯的虚拟身份生成
Bridging Privacy and Provenance: Traceable Virtual Identity Generation
在扩散生成过程中嵌入潜空间水印以携带可验证的身份签名，并配合3D几何与表情条件保持姿态一致性，为视觉研究者提供了一种在生成人脸时兼顾身份可控性与溯源验证的可复用思路。
原文依据 a lightweight latent watermarking mechanism that embeds an imperceptible identity signature during generation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期生成模型的进展使得高保真人脸生成成为可能，但构建既能保护用户隐私、又支持一致且可验证身份分配的可靠虚拟身份仍具挑战性。本文提出一个基于扩散的框架，用于生成可追溯的虚拟身份，在保持姿态与表情的同时维持稳定的身份语义。该框架将生成多样而一致身份嵌入的虚拟身份采样器，与保持输入人脸姿态及非身份特征的3D几何与表情条件模块相耦合。此外，我们引入一种轻量级潜变量水印机制，在生成过程中嵌入不可察觉的身份签名，使用户能够通过安全词元验证所生成虚拟身份的所有权，而无需暴露其真实人脸外观。定量评估表明，与先前工作相比，本方法实现了较高的虚拟身份一致性、较强的姿态与表情保真度以及更好的匿名性。这些结果验证了将虚拟身份采样、几何条件控制与潜变量水印集成到单一生成框架中的有效性，并凸显了该方案在构建隐私感知虚拟身份方面的实际潜力。
英文原文摘要 1399 字符
Recent advances in generative models have enabled the creation of high-fidelity human faces, yet constructing reliable virtual identities that preserve user privacy while supporting consistent and verifiable identity assignment remains challenging. In this paper, we propose a diffusion-based framework for generating traceable virtual identities that maintains stable identity semantics while preserving pose and expression. Our framework couples a virtual identity sampler that generates diverse yet consistent identity embeddings with a 3D geometric and expression conditioning module that preserves the pose and non-identity characteristics of the input face. In addition, we incorporate a lightweight latent watermarking mechanism that embeds an imperceptible identity signature during generation, enabling a user to verify ownership of the resulting virtual identity through a secure token without revealing their real facial appearance. Quantitative evaluations demonstrate that our method achieves high virtual identity consistency, strong pose and expression fidelity, and improved anonymity compared with prior works. These results validate the effectiveness of integrating virtual identity sampling, geometric conditioning, and latent watermarking into a single generative framework, and highlight the practical potential of our solution for constructing privacy-aware virtual identities.