SkinGPT
This page describes a feature of Haut.AI SaaS Platform that allows you to simulate skin effects for skincare, aging, and environmental impacts.
SkinGPT is offered as a premium feature.
Please contact your account manager to learn more about the pricing and activate this feature.
About the SkinGPT
SkinGPT is the first ever skin effects try-on system that utilizes advanced generative AI technologies, such as transformers, to simulate the effects of cosmetic skincare products, aging, and environmental impacts using clinical claims.
Read more about the available SkinGPT Models.
Areas of Application
SkinGPT can simulate any interventions affecting the skin including:
Environmental impacts (UV damage, air pollution)
Skincare product application effect
Aging
Change in specific skin parameters
Simulation Capabilities
Key simulation capabilities include:
Skin attributes (overall, U-zone, and T-zone): redness, pigmentation, uniformness, acne, lines, and skin tone.
Facial attributes: geometry changes in areas such as the under eyes, lips, nasolabial folds, eyebrows, and jawline.
Aging simulation: visualize changes from age 18 to 90, including:
Year-based progression (e.g., +10 years),
Targeted age simulations (e.g., 56 y.o.),
Animated aging process with 1-year increments.
Combined transformations that reflect multiple skin and face attributes simultaneously.
Check the full list of parameters and capabilities: Parameters of Simulations.
SkinGPT Terminology
Parameter — a single numeric or categorical value that defines a specific aspect of a level. Parameters control how an effect is visualized by shaping individual features. A collection of parameters forms a level, and adjustments to these parameters result in different simulation outputs.
Level — a vector of parameters that defines a specific transformation or state within a simulation. A level can represent a point in time (e.g., +10 years), or a variation in effect intensity (e.g., minimum, medium, or maximum severity). Each level corresponds to a distinct stage within an effect.
Effect — a group of related levels that together represent a full transformation category, such as aging, pigmentation, or acne. Effects provide the overall context, while levels define the granularity and progression within that context.
Availability of Application
SKinGPT available via API. See the detailed API doc: API for SkinGPT.
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