Privacy-Compliant Human Data Synthesis in Images for GDPR

Privacy regulations (e.g., GDPR, CCPA) mandate that public datasets with permissive commercial licenses (e.g., CC BY 4.0) containing humans be pseudonymized before use. However, existing anonymization methods have notable limitations: blurring or masking degrade downstream utility, GAN-based synthesis offers limited control and photorealism, and diffusion editors may retain identity traces. To overcome these limitations, we propose Rendering-Refined Stable Diffusion (RefSD), a three-stage pipeline that (1) removes real humans via segmentation and inpainting, (2) reconstructs pose-aligned, identity-free avatars through SMPL-based 3D rendering, and (3) refines appearance with text-guided diffusion for photorealism. By using rendering, RefSD provides explicit control over body shape, clothing, and pose, enabling diverse yet structured avatar generation. To validate human alignment, we introduce HumanGenAI, a human-annotation suite for evaluating privacy preservation, perceptual satisfaction, and attribute-generation fidelity. Beyond HumanGenAI, we conduct re-identification and downstream task benchmarks, demonstrating that RefSD matches the re-ID performance of complete masking while achieving competitive utility relative to real images. ... mehrTogether, RefSD and HumanGenAI establish a scalable pipeline and benchmark for privacy-compliant human synthesis in image datasets. Project: https:// rendering-refined-stable-diffusion.github.io/