Can Text-to-Image Generative Models Accurately Depict Age? A Comparative Study on Synthetic Portrait Generation and Age Estimation

Alexey A. Novikov; Artem Voronin; Fran\c{c}ois David; Miroslav Vranka

REVIEW 4 major objections 6 minor 1 cited by

Can Text-to-Image Generative Models Accurately Depict Age? A Comparative Study on Synthetic Portrait Generation and Age Estimation

T0 review · 4 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read The paper claims that text-to-image models can generate plausible faces with varied identities, but their depictions of a specified age are off by three to five years on average, so synthetic portraits are not yet reliable for high-stakes…

desk verdict A genuinely large synthetic-portrait benchmark whose headline MAE numbers are undercut by unvalidated age estimators, so the paper's central attribution of error to generators does not yet hold. read the letter →

arxiv 2502.03420 v1 pith:Z4QCS5CT submitted 2025-02-05 cs.CV

Alexey A. Novikov , Miroslav Vranka , François David , Artem Voronin This is my paper · ORCID

classification cs.CV

keywords text-to-imagegenerationageestimationsyntheticportraitsdemographicbiasgenerativemodelsMiVOLO-D1JAMportrait

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tests whether text-to-image models can depict a specified age by generating 12,960 synthetic portraits from prompts that fix age, nationality, and gender, then feeding those portraits to two age-estimation models. The central finding is that the generators are better at producing plausible, identity-varying faces than at hitting the requested age: average errors range from about 3 to 5.3 years depending on the model and estimator, and the largest, most erratic errors occur for prompts over 60. The authors conclude that current synthetic portraits are not reliable enough for high-stakes age-dependent tasks such as identity verification or training age-estimation models, unless they are heavily filtered and curated. They argue that such data may still be useful in exploratory or low-precision applications where exact age is not critical.

What carries the argument

The measurement pipeline is the prompt-to-estimator loop. A fixed prompt template ('Photorealistic selfie photo of a [age]-year-old [nationality] [gender] person, centered, high-resolution') is used to generate 12,960 portraits with three text-to-image models; two age estimators, MiVOLO-D1 and JAM, then produce a predicted age for each face. The argument is carried by the comparison between those predicted ages and the prompted age, summarized as MAE, RMSE, Pearson correlation, per-age-bucket and per-demographic breakdowns, and regression diagnostics. The load-bearing assumption is that the prompted age is the ground truth, so every reported error measures how far the generated face is from the age it was asked to show.

What would settle it

Take a subset of the generated portraits to human raters and ask them to estimate each face's age: if human estimates match the prompt ages closely while MiVOLO-D1 and JAM still show MAEs of 3 to 5 years, the paper's conclusion that generators fail at age depiction would be weakened, because the estimators themselves would be the untested link.

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Extended reading notes

Core claim

On its own terms, the paper shows that text-to-image generators can produce faces with varied identities, but the age those faces appear to be is only loosely controlled by the prompt. Across 12,960 prompts spanning 30 ages from 10 to 78, 212 nationalities, and both genders, the two age estimators give mean absolute errors between 2.99 and 5.26 years depending on the generator and estimator, with Epic Realism the most accurate and FLUX.1-dev the least. Older prompts (60+) show the largest errors and the most erratic correlations, and both estimators reveal gender and regional offsets in synthetic faces that the authors say are larger than what the same estimator shows on real faces. The conclusion the authors draw is that age fidelity, not identity fidelity, is the weak point of current text-to-image models for biometric use.

Load-bearing premise

The entire comparison treats the prompt age as ground truth and the two estimators' outputs as accurate measurements of the apparent age of synthetic faces, even though neither estimator was calibrated or validated on synthetic images.

Editorial extensions

If this is right

Age-estimation practitioners should not use text-to-image portraits as ground-truth training data without per-image filtering or calibration.
Teams choosing a generator for age-sensitive work should prefer Epic Realism over FLUX.1-dev based on these error ranges, and should treat 60+ prompts as unreliable.
Age-verification systems that use synthetic data should only act on large age mismatches, since small-to-moderate errors are common.
The reported pattern motivates post-hoc correction or targeted prompt engineering for the older-age range, where MAE and correlation degrade most.
Synthetic portraits remain usable for exploratory tasks where approximate age distributions are enough.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct extension the authors leave open is a human-rater study on a sample of the same portraits; it would separate generator error from estimator miscalibration on synthetic faces.
The same prompt protocol could be rerun on newer text-to-image models to see whether age fidelity improves or whether the reported gender and regional offsets persist.
If the pattern generalizes, prompt-age error could serve as a cheap, image-only proxy for demographic fairness auditing of generative models, since systematic MAE offsets by gender and region are measurable without real-image data.
Adding known-age real portraits through the same estimators would provide a baseline the paper does not include, clarifying how much of the reported error is specific to synthetic faces.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a genuinely new benchmark—12,960 prompts, 212 nationalities, 30 ages, three generators, two estimators—and the scale alone makes it useful to anyone working on synthetic data for biometrics. The authors also deserve credit for reporting demographic breakdowns and outlier rates rather than just a single MAE. The paper is readable and the cautionary direction is plausible.

But the load-bearing inference has a hole. The paper treats the age estimators as ground truth for apparent age and attributes all error to the generators. No one validates MiVOLO-D1 or JAM on synthetic faces. No human-rater study. No real-data baseline. The authors even state in Ethical Impact C that JAM's accuracy decreases in older populations, which is exactly the kind of estimator bias that could inflate MAE at the age extremes where they report the worst results. Without calibration, the headline 2.99–5.26 year MAE range is uninterpretable: it could be generator failure, estimator domain shift, or both. The stress-test note is right.

There are smaller problems too. JAM is proprietary and self-cited, so the second judge is a black box. No code, prompts, seeds, or dataset are released, and there are no confidence intervals on any metric. The paper also claims identity consistency is preserved, but I did not see an actual identity metric. The t-tests are mentioned but not shown.

What holds up: the prompt-age-vs-predicted-age regressions, the age-bucket breakdowns, and the outlier analysis are internally consistent. The qualitative finding that different generators have different age-bias profiles is believable and worth pursuing. The central caution about synthetic data for high-stakes age verification is reasonable, but it needs to be reframed as a joint property of generators and estimators.

Bottom line: this paper deserves a serious referee, not a desk reject. The benchmark is too large and too relevant to ignore. But the referee should ask for estimator validation on synthetic images (human raters or a real-image control), confidence intervals, a released dataset or at least code and prompt list, and a reanalysis that separates generator error from estimator bias. I would not cite the headline numbers until that is done.

Referee Report

4 major / 6 minor

Summary. The paper evaluates whether three text-to-image models (FLUX.1-dev, Stable Diffusion 3.5 Large, and SDXL Epic Realism) can generate portraits matching a specified age. The authors construct 12,960 prompts spanning 30 ages from 10 to 78, 212 nationalities, and both genders; generate one image per prompt; and score the resulting faces with two age estimators, MiVOLO-D1 and JAM. They report MAE, RMSE, and Pearson correlation for each estimator-generator pair, finding the best alignment for Epic Realism (MAE 2.99 with MiVOLO-D1, 3.52 with JAM) and the worst for Flux (MAE 5.26 and 4.68), with larger errors and low correlations at age extremes, demographic biases, and a small fraction of decade-level outliers. They conclude that current synthetic portraits are too unreliable for high-stakes age-related tasks without significant filtering and curation.

Significance. The study addresses a timely and practical question, and its breadth is a genuine strength: 30 ages, 212 nationalities, three generators, two estimators, and demographic subgroup analyses. The authors are appropriately cautious in their conclusion and provide useful diagnostics beyond global metrics, including age-bucket breakdowns, outlier counts, and regression slopes. However, the central claim depends on treating age-estimator outputs on synthetic faces as ground-truth apparent age; no human-rater validation, no calibration of the estimators on synthetic images, and no quantitative real-image baseline are provided. Because both estimators are trained predominantly on real photographs and one is proprietary and co-authored by the same team, the reported MAE range is best interpreted as a joint generator-estimator alignment score. If the estimator domain-shift concern is addressed with perceptual ratings or a real-image control, the paper would be a sound and useful cautionary benchmark for practitioners.

major comments (4)

[Section III.B and Section IV.A] The paper's load-bearing assumption is stated in Section III.B: 'we treat the prompted age as the ground truth,' and then all MAE/RMSE/correlation values in Table IV are computed against the outputs of MiVOLO-D1 and JAM. This assumes both estimators are unbiased readers of apparent age on synthetic faces, but no evidence for that assumption is provided. Age estimators trained on real photographs are known to compress toward the training distribution on out-of-distribution inputs, which would produce exactly the pattern reported here: overestimated young prompts, underestimated old prompts, inflated MAE, and low correlation at age extremes. The authors themselves flag in Ethical Impact C that age estimation accuracy may decrease in older populations. The authors should add a synthetic-image calibration using human perceptual age ratings and/or a quantitative real-portrait baseline scored by the same estimators. Without such controls, the headline 2.99-5.26 year MAE range cannot be attributed to generator failure rather than evaluator domain shift.
[Section III.B and reference [12]] JAM is a proprietary age estimator developed by members of the author team (reference [12]), and the paper provides no architecture, training data description, or reported performance on synthetic images. Section IV.A uses JAM as one of two judges for every generator ranking, yet an independent reader cannot reproduce or scrutinize its behavior. Section IV.B claims that JAM 'broadly tracks age variations' on real-life production data but gives no quantitative results for that claim. The authors should either disclose sufficient technical detail about JAM, base the main claims on publicly available estimators with published error characteristics, or add a table comparing JAM's real-image MAE/correlation by age bucket so that estimator bias can be separated from generator error. This is a transparency and reproducibility issue, not a novelty issue.
[Section IV.A (Table IV) and Section IV.D] Table IV reports large differences between generator-estimator combinations (e.g., Epic Realism MAE 2.99 versus Flux MAE 5.26 for MiVOLO-D1), but no confidence intervals, standard errors, or sample-size information are given for these metrics. The t-tests mentioned in Section IV.D are not reported with test statistics, p-values, or multiple-comparison corrections. Since the relative ranking of generators is a central conclusion, the authors should provide bootstrap confidence intervals for MAE/RMSE/correlation and a table of the t-test results. In addition, the age-bucket correlations in Section IV.B are computed within narrow 10-year ranges, which mechanically restricts the achievable correlation; the interpretation that generators 'struggle to differentiate subtle changes' should be checked against the estimator's own consistency on real faces over the same age spans.
[Abstract and Section V] The abstract and conclusion claim that text-to-image models 'can consistently generate faces reflecting different identities' and preserve 'identity-related cues,' but no identity-consistency metric appears in the methodology. The experiments vary age, nationality, and gender in prompts, yet there is no face-recognition embedding similarity measure, no repeated-generation test, and no controlled comparison linking the same identity across conditions. This positive claim is presented alongside the main negative result, so it should either be removed or supported with a concrete measurement; as written, it exceeds the evidence in the paper.

minor comments (6)

[Section III.A] The text says '30 distinct ages from 10 to 78,' but 10 through 78 inclusive contains 69 integer ages; the authors should list the specific 30 ages or explain the sampling rule to remove ambiguity.
[Section IV.B] The age-bucket narrative discusses 10-19, 20-29, 30-39, 40-49, 50-59, and 70-79 but skips 60-69; the authors should clarify whether the 60-69 bucket was omitted, merged, or simply not highlighted.
[Section IV.C] The outlier definition uses a deviation threshold of 'more than 11 years' without justification; a brief rationale or a sensitivity analysis for thresholds such as 8, 10, and 12 years would strengthen the robustness of the outlier discussion.
[Ethical Impact Statement, Section A] The sentence beginning 'One aspect to assess from a risk perspective is potential bias...' is duplicated verbatim in the same section; the duplicate should be removed.
[Figure 2] The scatter plots would be easier to interpret if each panel included the MAE, sample size, and regression equation, and if the axis labels and point density (e.g., alpha or hex-bin) were specified.
[General] No code or dataset is provided, and the dataset is only 'aimed' to be released in the future; given the paper's benchmarking character, releasing prompts, generation parameters, and per-image age estimates would materially improve reproducibility.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: the MAE/RMSE comparisons are an external benchmark of T2I age fidelity, with only a minor self-citation (JAM) that is not load-bearing.

full rationale

The paper's quantitative claims are an empirical comparison between prompted ages and the outputs of two age-estimation models, MiVOLO-D1 (external) and JAM (the authors' proprietary model). No parameter is fitted from the reported test data and then renamed a prediction; no equation defines the conclusion in terms of itself; no uniqueness theorem or ansatz is imported from prior author work. The step 'we treat the prompted age as the ground truth' is a transparent operational convention rather than a hidden self-reference, because the prompted age is an independent input to the generation process, not an output of the estimator being evaluated. The only mild self-citation concern is that JAM, one of the two judges, is the authors' own model and is supported mainly by a statement about real-world and NIST performance rather than by a calibration study on synthetic faces. That is a measurement-validity threat for external validity, but it does not make the derivation circular because MiVOLO-D1 independently corroborates the headline pattern and the numerical results are not constructed to equal their own inputs. The authors' own caveat that age estimation accuracy may decrease in older populations reinforces the validity concern without establishing circularity. No circular step meets the evidentiary bar, so score 2 reflects the minor self-citation rather than a circular derivation.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central claim rests on two domain assumptions: prompt age is the intended age, and estimator output measures apparent age. Neither is independently validated for synthetic images in this paper. The only hand-chosen numeric parameter is the 11-year outlier threshold. No new theoretical entities are introduced.

free parameters (1)

Outlier deviation threshold = 11 years
Chosen by the authors to define an outlier as a predicted age deviating from the prompt age by more than 11 years (Section IV.C); no justification or sensitivity analysis is provided, and the outlier percentages depend on this threshold.

assumptions (5)

domain assumption Predicted ages from MiVOLO-D1 and JAM are valid measures of the apparent age of synthetic faces.
All error metrics in Section IV.A treat estimator outputs as the age a human viewer would perceive, but the estimators are not calibrated on synthetic images and no human rating is reported.
domain assumption The prompt age is the correct target age for each synthetic portrait.
Section III.B states that because images are synthetic, the prompted age is treated as the ground truth, equating the requested age with the visually depicted age.
domain assumption Nationality and gender labels in prompts are faithfully rendered in the generated faces.
Demographic subgroup analyses in Section IV.B and IV.D assign biases to gender and region based solely on prompt labels, without checking that the generated face visually matches the requested nationality or gender.
standard math The reported t-tests and linear regressions satisfy standard statistical assumptions.
Section IV.D mentions t-tests and regression analyses but does not report distributions, independence, or multiple-comparison corrections, so the validity of the significance claims depends on these unstated conditions.
domain assumption The real-life production data used as a comparison is a fair baseline.
Section IV.B contrasts synthetic results with production data but gives no sample size, collection details, or metrics, so the comparison assumes the baseline is representative and comparable.

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Cite this review

Pith. "Pith review of Can Text-to-Image Generative Models Accurately Depict Age? A Comparative Study on Synthetic Portrait Generation and Age Estimation." pith.science (2026). https://pith.science/paper/Z4QCS5CT

@misc{pith2026250203420,
  author       = {Pith},
  title        = {Pith review of: Can Text-to-Image Generative Models Accurately Depict Age? A Comparative Study on Synthetic Portrait Generation and Age Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z4QCS5CT}},
  note         = {Machine review of arXiv:2502.03420}
}

read the original abstract

Text-to-image generative models have shown remarkable progress in producing diverse and photorealistic outputs. In this paper, we present a comprehensive analysis of their effectiveness in creating synthetic portraits that accurately represent various demographic attributes, with a special focus on age, nationality, and gender. Our evaluation employs prompts specifying detailed profiles (e.g., Photorealistic selfie photo of a 32-year-old Canadian male), covering a broad spectrum of 212 nationalities, 30 distinct ages from 10 to 78, and balanced gender representation. We compare the generated images against ground truth age estimates from two established age estimation models to assess how faithfully age is depicted. Our findings reveal that although text-to-image models can consistently generate faces reflecting different identities, the accuracy with which they capture specific ages and do so across diverse demographic backgrounds remains highly variable. These results suggest that current synthetic data may be insufficiently reliable for high-stakes age-related tasks requiring robust precision, unless practitioners are prepared to invest in significant filtering and curation. Nevertheless, they may still be useful in less sensitive or exploratory applications, where absolute age precision is not critical.

Figures

Figures reproduced from arXiv: 2502.03420 by the authors.

**Figure 1.** Examples of generated images using different methods: Flux (a, b), Stable Diffusion 3.5 Large (c, d), and SDXL Epic Realism (e, f). [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Prompt vs. Predicted Age plots with regression lines for two T2I [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images
cs.CV 2025-05 conditional novelty 6.0 of 10

FLUXSynID generates paired document-style and live synthetic face images with user-controllable identity attributes and releases a dataset of 14,889 identities.

Reference graph

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