# Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data

arXiv: https://arxiv.org/abs/2412.06248 · HTML: https://mlsanity.com/paper/2412.06248/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 11 of 11)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [On the Challenges and Opportunities in Generative AI](https://mlsanity.com/paper/2403.00025/) (2024)
- [Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget](https://mlsanity.com/paper/2407.15811/) (2024)
- [Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions](https://mlsanity.com/paper/2407.04103/) (2024)
- [Latent Diffusion Models for Attribute-Preserving Image Anonymization](https://mlsanity.com/paper/2403.14790/) (2024)
- [Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion](https://mlsanity.com/paper/2311.00056/) (2023)
- [Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data](https://mlsanity.com/paper/2405.03243/) (2024)
- [A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report](https://mlsanity.com/paper/2208.01636/) (2022)

## Built on by (most cited first, 2 of 2)

- [Unsafe2Safe: Controllable Image Anonymization for Downstream Utility](https://mlsanity.com/paper/2603.28605/) (2026)
- [Diffuse the object, keep its label: curating detector training data from a few unlabeled photographs via VLM-built 3D vegetation scenes](https://mlsanity.com/paper/2608.09691/) (2026)

## Abstract (arXiv)

Growing privacy concerns and regulations like GDPR and CCPA necessitate pseudonymization techniques that protect identity in image datasets. However, retaining utility is also essential. Traditional methods like masking and blurring degrade quality and obscure critical context, especially in human-centric images. We introduce Rendering-Refined Stable Diffusion (RefSD), a pipeline that combines 3D-rendering with Stable Diffusion, enabling prompt-based control over human attributes while preserving posture. Unlike standard diffusion models that fail to retain posture or GANs that lack realism and flexible attribute control, RefSD balances posture preservation, realism, and customization. We also propose HumanGenAI, a framework for human perception and utility evaluation. Human perception assessments reveal attribute-specific strengths and weaknesses of RefSD. Our utility experiments show that models trained on RefSD pseudonymized data outperform those trained on real data in detection tasks, with further performance gains when combining RefSD with real data. For classification tasks, we consistently observe performance improvements when using RefSD data with real data, confirming the utility of our pseudonymized data.