Parameters of Simulations
SkinGPT offers a comprehensive set of parameters that can simulate various skin effects including environmental impacts, skincare product application, aging, and specific skin parameter changes. The system supports simulations for multiple parameters including:
Redness
Pigmentation
Uniformness
Acne
Lines
Skintone
Face Geometry
Aging
Skin Aging (option of aging parameter)
UV Index (option of aging parameter)
Weight (option of aging parameter)
Hair graying (option of aging parameter)
Most of these parameters can be combined.
List of Parameters
Redness
Supported in SkinGPT and SkinGPT High Resolution models.
Can be configured to show positive and negative effect.
Positive adjustment calms erythema, yielding a more even complexion that reflects reduced inflammation. Negative adjustment heightens flushing to simulate flare-ups typical of rosacea or irritation.
Can be configured for:
Whole face.
T-zone (forehead, nose, and chin).
U-zone (cheeks and jawline).
Pigmentation
Supported in SkinGPT and SkinGPT High Resolution models.
Can be configured to show positive and negative effect.
Positive lightens discoloured areas, portraying a clearer, more uniform tone after brightening care. Negative deepens spots and patches, replicating sun damage or hormonal hyper-pigmentation.
Can be configured for:
Whole face.
T-zone (forehead, nose, and chin).
U-zone (cheeks and jawline).
Sun Spots
Supported in SkinGPT and SkinGPT High Resolution models.
Can be configured to show positive effect only.
Positive lightens solar lentigines and mottled pigmentation, yielding a clearer, more even complexion that suggests successful photodamage reversal. Negative darkens and multiplies sun spots, conveying cumulative UV exposure or inadequate protection.
Can be configured for:
Whole face.
Forehead.
Left cheek.
Right cheeks.
Nose.
Perioral.
Uniformness
Supported in SkinGPT and SkinGPT High Resolution models.
Can be configured to show positive and negative effect.
Positive smooths colour transitions for a balanced, consistent skin surface. Negative introduces blotchiness and visible tone variation, highlighting uneven texture or PIH.
Can be configured for:
Whole face.
T-zone (forehead, nose, and chin).
U-zone (cheeks and jawline).
Acne
Supported in SkinGPT model only. Not supported in SkinGPT High Resolution model.
Can be configured to show positive and negative effect.
Positive removes comedones and pustules, illustrating clearer skin following effective therapy. Negative adds lesions to depict breakout severity or product misuse.
Can be configured for:
Whole face.
T-zone (forehead, nose, and chin).
U-zone (cheeks and jawline).
Lines
Supported in SkinGPT and SkinGPT High Resolution models.
Can be configured to show positive effect only.
Positive softens fine lines and wrinkles for a rejuvenated appearance. Negative accentuates creases and furrows, conveying progressive intrinsic ageing or dehydration.
Can be configured for:
Whole face.
T-zone (forehead, nose, and chin).
U-zone (cheeks and jawline).
Skintone
Supported in SkinGPT and SkinGPT High Resolution models.
Can be configured to show positive and negative effect.
Positive brightens and balances the base complexion, reducing dullness and unwanted undertones. Negative introduces sallowness or uneven hue to showcase fatigue or environmental stress.
Can be configured for:
Whole face.
T-zone (forehead, nose, and chin).
U-zone (cheeks and jawline).
Face Geometry
Supported in SkinGPT model only. Not supported in SkinGPT High Resolution model.
Positive restores volume and firmness, subtly lifting contours to suggest youthful plumpness. Negative models volume loss and sagging, simulating gravitational ageing or weight change.
Can be configured for:
Lips
Nasolabial Folds
Eyebrows
Jaw
Weight
Supported in SkinGPT model only. Not supported in SkinGPT High Resolution model.
Positive adjustments create a slimmer look. Negative adjustments add weight to the face.
Works only when the Aging parameter is enabled. Does not work when the Skin Aging is turned on.
Supported range
From -100 to 100 in conditional units.
Hair graying
Supported in SkinGPT model only. Not supported in SkinGPT High Resolution model.
Adjustments cause hair to appear grayer.
Works only when the Aging parameter is enabled. Does not work when the Skin Aging is turned on.
Supported range
From -100 to 0 in conditional units.
Aging
Supported in SkinGPT model only. Not supported in SkinGPT High Resolution model.
Can be configured for:
Age Shift
Adjusts the perceived age relative to the current age.
Example: A shift of +10 will age the face by 10 years; -5 will rejuvenate it by 5 years.
Specific Age
Directly sets the target age for visualization.
Example: Setting age to 45 will simulate the person looking approximately 45 years old, regardless of their current age.
Positive reverses multiple age markers—fewer wrinkles, spots, and laxity—producing a younger look. Negative advances these markers in concert to portray accelerated ageing.
Supported age range
From 20 to 70 years old.
Values outside of this range may not be supported or could produce inaccurate results.
Skin Aging
Allows aging to be applied to the facial skin only, without changing basic personality traits such as facial shape, eye or hair color.
Supported in SkinGPT model only. Not supported in SkinGPT High Resolution model.
Skin Aging is an option of Aging parameter and work with Age Shift only.
UV-index
Supported in SkinGPT model only. Not supported in SkinGPT High Resolution model.
Works only when the Aging parameter is enabled.
When UV Index is turned on, the Pigmentation parameter will be ignored, as pigmentation changes are derived from the UV simulation logic.
Accepts values from 0 to 11+, following the global UV index scale.
0–2: Low UV exposure
3–5: Moderate exposure
6–7: High exposure
8–10: Very high exposure
11+: Extreme exposure
Positive and negative effect explanation
SkinGPT uses a relative scale from -100 to +100 to simulate changes in skin conditions. Here’s how to interpret and apply this scale across supported parameters.
Key Principles
0: Represents the user’s original, unaltered state.
+100: Simulates a strong improvement in the condition — enough to move the result into the “problem-free” group based on the underlying metric distribution.
–100: Simulates a strong worsening of the condition — enough to place the result into the “severe issue” group.
Relative vs Absolute Change
Important: The effect of a +100 or -100 adjustment does not translate to an exact numeric shift in the metric (e.g., not always +20 or –20 points). The outcome depends on the initial distribution of that metric in the dataset.
Each skin metric (e.g., redness, pigmentation, lines) has its own distribution curve (often centered around a mean like 60–70).
Applying +100 will push the simulated result into the upper quantile (best visible outcome), and –100 into the lower quantile (worst visible outcome).
Visual Analogy
Think of each metric as a bell curve:
+100 shifts the individual to the right end of the curve (least concern).
–100 shifts them to the left (highest concern).
The same shift amount can produce different absolute outcomes depending on where the person starts on the curve.
Notable Clarifications
A person with moderate redness and another with severe redness will both experience improvement with +100, but the final result may not be identical.
The scale is normalized per metric and designed to be intuitive, but not linear in effect.
Exception Cases
Some metrics (e.g., Sunspots, ITA (Individual Typology Angle)) use a linear scale and do not follow the distribution-based transformation model described above.
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