The Shroud of Turin · Chapter 7 of 21

The digital autopsy: reconstructing the body from the image

What computer analysis of the Shroud’s image can show about the body beneath it, what it cannot show, and how to tell the difference

No facial reconstruction is shown here. The available infographic claims precise facial measurements and high accuracy, but the studies discussed in this chapter do not support those claims. They support limited analysis of image tone and possible distance relationships; they do not recover a uniquely identifiable face.

The image on the Shroud encodes something about distance. Where the cloth lay closer to the body the image is darker, and where it lay farther away it is lighter. If that relationship holds, the image can be read as a kind of depth map, and digital tools can turn that map into a three-dimensional model of a body. That is the idea behind what has been called a digital autopsy.

The idea is powerful, and it is easy to overstate. This chapter sets out what the image supports, how the digital methods work and where they can go wrong, and what a reconstruction would need before it could be treated as evidence about the man on the cloth.

What the earlier work established

Four findings make digital reconstruction worth attempting.

The image is a negative. Secondo Pia’s 1898 photographs showed that the body image reverses when photographed, so a positive view of the image shows a lifelike figure.¹
The colour is superficial. The image lies on the outermost fibres of the linen, and the 1978 STURP investigation found no paint, dye, or binder in it.²
Intensity follows distance. The 1976 VP-8 analysis by John Jackson and Eric Jumper turned the image into a relief of a face, and a later measurement reported a correlation of about 0.6 between image density and modelled cloth-to-body distance at thirteen facial points.³⁴
The effect is reproducible by other methods. The Italian computer scientist Giovanni Tamburelli processed the 1931 Enrie photographs with digital algorithms in 1981 and obtained a coherent relief, independently of the VP-8.⁵ In 2025 a principal-component analysis of the 2002 Durante photograph found that almost all of the image’s information lies in a single intensity variable.⁶

Together these results show that the image’s tonal structure is systematic. They do not show that the structure is a body, or that the body can be recovered from it in any particular detail.

The digital pipeline

A digital reconstruction moves through several stages. Each stage adds assumptions, and each one can change the result.

Stage	What is done	What it can change
1. Source images	Choosing a photograph: the 1931 Enrie prints, the 1978 STURP photographs, or the 2002 Durante photograph	Each source has different lighting, resolution, and exposure history
2. Cleaning	Reducing the weave pattern, masking the 1532 fire scorches and water stains, and removing repairs and damage	Filtering can remove real structure or create false structure
3. Height conversion	Mapping image intensity to a value interpreted as height or distance	The mapping is a choice, and the output depends on it
4. Body fitting	Fitting a body model to the relief, usually with draping and posture assumptions	Posture, landmark choice, and the model’s proportions
5. Detail addition	Adding skin texture, hair, wounds, or a face	Anything added at this stage is not in the image data
6. Checking	Overlaying the model on the image and comparing	A visual match can be produced by an adjusted model, so overlay checks do not prove the model

Stages 1 to 4 are where measurement is possible, and where the work can be checked against the photographs. Stage 5 is where the output stops being a measurement and starts being an interpretation. Stage 6 is a consistency check, not an independent test.

Measuring the relationship

The 1984 study by Jackson, Jumper, and William Ercoline built physical models of a body and photographed linen draped over them under controlled conditions. That gave empirical distances, not only calculated ones. The authors extended the analysis to a larger set of points and tested whether a single function could describe the relationship across the frontal image.⁴

The 1984 paper is also clear about its limits. The strongest numbers came from a small facial sample, and the distances were computed from a reference model rather than measured from the cloth. The dorsal image was not given the same analysis. The authors said that no single mechanism accounted for all the observations. The relationship between image density and distance is a necessary condition for any formation hypothesis, not a sufficient one.⁴

A correlation of about 0.6 across thirteen points means that distance accounts for roughly 60 per cent of the variation at those points. It does not mean the image is a precise map of the body. The confidence interval is wide when the sample is small, and a larger replication has not yet been published.

Body size and proportions

Digital reconstruction can estimate the stature of the figure. The Jackson and Jumper model gives a height of roughly 178 to 180 centimetres. A later analysis by Fanti, Basso, and Bianchini, which fitted landmarks to a computerised manikin and corrected for cloth wrapping and posture, gave about 175 centimetres, with a margin of two centimetres.⁷ The two results are close, but they rest on different assumptions about posture, landmarks, and cloth geometry, so the figure should be read as a range, not a measurement.

The frontal image measures about 1.95 metres and the dorsal image about 2.02 metres. These lengths cannot be read directly as stature, because the cloth is not a rigid shell. The manikin analysis showed that the two images can be made compatible with one body under plausible assumptions about the head, the knees, and the feet. That is a demonstration of consistency, not a proof that the two images show one body.⁷

The challenge from draping and relief

The most important objection to the depth reading is that the image may have been made from a relief rather than a draped body. In 2025 Cicero Moraes used open-source body models and fabric simulation to compare a full body with a low-relief model. He reported that the low-relief model gave a contact pattern closer to the image, with less distortion.⁸

Casabianca, Marinelli, and Piana answered that the study modelled only the frontal image, treated contact as the only possible mechanism, and did not address the chemistry of the image. Moraes replied that his scope was limited to contact geometry.⁹ The geometric debate is open, and the chemical question is separate. A contact model that fits the geometry still has to account for the superficiality of the colour, the absence of pigment, and the fact that the blood came before the image.

A related objection concerns the image in places the cloth would not have touched a relief, such as the hollows of the eyes and the space under the nose. Jackson raised this as a gap in the bas-relief explanation in 1990.¹⁰ The answer to that gap has not yet been settled.

Overlay checks and what they can show

A common check places a reconstructed body over the image at partial transparency and asks whether the features line up. The nose should fall in the darkest area, the eye sockets in the lighter areas, and the wounds should follow the gravity paths of the blood. Colour-channel overlays make small mismatches visible.

The overlay is a useful test of consistency, but it cannot separate data from invention. A model can be adjusted until the features fit, and an artist who draws a face can make it fit a dataset that was used to guide the drawing. A reconstruction that uses a generative AI tool adds a further problem, because the tool was trained on modern human images and fills in details such as skin texture and facial geometry that are not in the cloth. A good overlay therefore does not show that the detail came from the image.

Wounds added to a model

Some reconstructions add wounds to the model at places where the cloth shows marks, and then use the wounds as a check on the body’s proportions. The logic is that a blood path running down a model’s head should follow the head’s shape, and a scourge mark should fall on a plausible part of the back.

That logic works as a consistency test only if the wounds are taken from the cloth and placed without adjustment. When the wounds are painted or inpainted by an AI tool, the test becomes circular: the model is adjusted to fit wounds the artist placed, and the wounds are then cited as confirmation. Wound-marks added by this route are interpretation, and they cannot be counted as evidence on the same footing as the measured relief.

The face

The most public outputs of digital reconstruction are faces. Several have been published, some as AI-generated portraits described as based on Shroud data. Those images attract attention because they show what the viewer expects a face to look like, and the detail they contain is largely supplied by the generator.

There are two separate issues. The first is methodological. A face reconstructed from a depth-like map of the cloth can show the broad shape of the head, but the features that matter most to recognition, such as the exact shape of the nose and the form of the eyelids, depend on details the image does not measure reliably. The second is pastoral. An image of Jesus generated by software and presented as the face of the Shroud risks putting an artefact in the place of the person. The Shroud is best read as physical evidence about a man who suffered and died, not as a portrait that the viewer can complete.

The image in the panel at the top of this chapter is an interpretive reconstruction. It should be read as an artist’s informed model, not as a measurement.

An example from the author’s own work

Otangelo Grasso has published an AI-assisted reconstruction of the Shroud figure, and it is one of the most complete attempts of its kind. It documents a workflow from image preparation through overlay checks, colour-channel comparison, and wound integration, and it treats the Shroud as the fixed reference that the model must satisfy.¹¹

The reconstruction is self-published and has not been independently reviewed. Its own text acknowledges that AI image generators can introduce anatomical conventions that are not in the cloth, and that the overlay cannot fully separate what the cloth encodes from what the tool supplies. Readers should weigh it as a documented method and as an interpretation, not as a measurement of the man.

What a credible study would need

A digital reconstruction that aims to be evidence needs more than a convincing image. The following conditions would make its results testable:

Raw data first. The height conversion and the body model should be applied to minimally processed images, and the results should be reported with and without each filter.
Full-image analysis. The frontal and dorsal images should both be analysed. The 1984 correlation used thirteen facial points, and it has not been replicated across the full image with modern body models.⁴
Independent replication. Another group should run the same pipeline on the same data and report the outcome, including failures.
Principal-component preprocessing. Reducing the image to its dominant intensity variable before correlation would make the analysis less sensitive to colour and channel choices.⁶
Falsifiable predictions. A draped body should produce lateral distortion at the flanks, and a bas-relief should not produce image in the hollows of the eyes. Each of these can be checked against the image.
No generated detail in the data. Any model built from the image should be kept separate from any detail added by a generator, and the two should be reported separately.

What digital analysis can and cannot show

Digital analysis can show that the Shroud’s image has a systematic tonal structure, that the structure is consistent with a relationship between image density and distance, and that the relief is compatible with a body of a broadly adult size. It can show that different analyses converge on a common picture of the figure’s proportions.

It cannot, on its own, identify the man, determine the cloth’s date, or explain how the image was formed. The stages in which detail is added, such as faces and wounds, depend on assumptions and on tools that can supply their own content. A reconstruction is a model of the cloth, and its value depends on how carefully the model is separated from the interpretation.

References

Pia, S. (1898). The first photograph of the Holy Shroud. The 1898 photographs and the reports of the exhibition are discussed in the historical literature.
Schwalbe, L. A., & Rogers, R. N. (1982). Physics and chemistry of the Shroud of Turin: A summary of the 1978 investigation. Analytica Chimica Acta, 135, 3–49.
Jackson, J. P., Jumper, E. J., Mottern, R. W., & Stevenson, K. E. (1977). The three-dimensional image on Jesus’ burial cloth. In K. E. Stevenson (Ed.), Proceedings of the 1977 United States Conference of Research on the Shroud of Turin (pp. 74–94). Holy Shroud Guild. shroud.com/pdfs/jackson.pdf
Jackson, J. P., Jumper, E. J., & Ercoline, W. R. (1984). Correlation of image intensity on the Turin Shroud with the 3-D structure of a human body shape. Applied Optics, 23(14), 2244–2270. doi:10.1364/AO.23.002244
Tamburelli, G. (1981). Some results in the processing of the Holy Shroud of Turin. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-3(6), 670–676. doi:10.1109/TPAMI.1981.4767157
McAvoy, T. (2025). Artificial intelligence analysis of images of the Shroud of Turin. International Journal of Archaeology, 13(1). doi:10.11648/j.ija.20251301.17
Fanti, G., Basso, R., & Bianchini, G. (2010). Turin Shroud: Compatibility between a digitized body image and a computerized anthropomorphous manikin. Journal of Imaging Science and Technology, 54(5), 050503. doi:10.2352/J.ImagingSci.Technol.2010.54.5.050503
Moraes, C. (2025/2026). Image formation on the Holy Shroud: a digital 3D approach. Archaeometry, 68(1), 84–93. doi:10.1111/arcm.70030
Casabianca, T., Marinelli, E., & Piana, A. (2026). Commentary on the article ‘Image Formation on the Holy Shroud—A Digital 3D Approach’ by Cicero Moraes. Archaeometry. doi:10.1111/arcm.70109 Moraes’s reply: doi:10.1111/arcm.70112
Jackson, J. P. (1990). Is the image on the Shroud due to a process heretofore unknown to modern science? Shroud Spectrum International, 34, 3–29. shroud.com/pdfs/jackson90.pdf
Grasso, O. (2025). The Shroud Reimagined: A Forensic Reconstruction. Self-published. Not independently peer reviewed.