Bridging Privacy and Provenance: Traceable Virtual Identity Generation

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.