Realistic Skin Trouble Simulation Via Image Generation Models

Skin troubles and disorders such as acne, pigmentation, and eczema not only affect appearance but also cause physical discomfort and influence self-perception. These challenges have driven significant research in dermatology, particularly in the development of treatment strategies and skincare products. However, the simulation and prediction of their progression remain relatively underexplored. Improving the ability to model the progression of skin disorders can support clinical decision-making, enhance patient awareness, and promote treatment adherence, thereby contributing to progress in dermatological research. In this study, we propose a pipeline for simulating the progression of skin disorders using generative models. Our approach involves fine-tuning a diffusion model to create a comprehensive multi-paired dataset of skin disorders and training a GAN to simulate severity transitions. As a result, we generate photorealistic images that accurately depict the progression of skin disorders with precise control over severity levels.