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Photorealistic image synthesis is the creation of synthetic images meant to look like photographs to human viewers. The image may come from traditional computer graphics, which models geometry, materials and light, or from an AI generator trained on data. What defines it is how the image appears. The label says nothing about how it was made, and it does not show that the scene or person in it ever existed.
The two standard definitions
Two research definitions cover most of the term’s meaning. One comes from computer graphics. The other comes from perception research.
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The computer graphics definition
Marini, Rizzi and Rossi describe photorealistic image synthesis as a field of computer graphics “whose aim is to produce synthetic images that are undistinguishable from real ones.” That is the authors’ description, not a formal standard. They tie photorealism to accurate computational models of how light interacts with materials in a geometrically described scene. They discuss ray tracing and radiosity as ways to model parts of the light distribution (Marini et al., SPIE, 2001).
The perceptual definition
Shaojing Fan and co-authors define visual realism as “the extent to which an image appears to people as a photo rather than computer generated.” Their paper builds a reference-free prediction framework and a benchmark of 2,520 images with human-annotated attributes. It appeared in IEEE Transactions on Pattern Analysis and Machine Intelligence in 2018 (PubMed record).
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Under this definition photorealism is an outcome that people judge. It does not guarantee a particular production method. The same image can be rated differently by different viewers and under different viewing conditions.
Two routes to a photographic look
| Route | How the image is created | What to compare |
|---|---|---|
| Physically based computer graphics | Models geometry, materials and light transport, then maps the computed result to a display or print. | Fidelity of material and lighting models, control over the scene, rendering cost, tone-mapping behavior |
| Learned image synthesis | A trained generative model produces or edits images, often conditioned on text or another input. | Human-rated photographic appearance, prompt alignment, artifacts, controllability, provenance and disclosure |
The routes differ in what is modeled explicitly and what is learned from data. They are not always exclusive in practice. Neither is inherently more photorealistic, so compare them on a specific task and the evidence for it.
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For the learned route, the 2022 GLIDE study is a useful example. Human evaluators preferred classifier-free guidance over CLIP guidance for both photorealism and caption similarity, and the authors reported that it often produced photorealistic samples (Nichol et al., PMLR, 2022). That result applies to that study and model. It is not a current ranking of image generators.
Why the display matters
A physically computed light field has a much wider range than a screen or print can show. Marini and colleagues identify tone reproduction as the challenge of compressing an extended-range scene into displayable colors. Accurate rendering calculations therefore do not fully determine what a viewer sees (Marini et al.).
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How photorealism is evaluated
None of the sources reviewed sets a universal pass/fail threshold or numeric cutoff. Researchers use several approaches.
- Human judgment. Observers decide whether an image looks like a photograph. This matches the perceptual definition directly.
- Controlled perceptual experiments. Rademacher and colleagues varied shadow softness, surface smoothness, number of light sources, number of objects and variety of object shapes to see how each affected perceived realism (Microsoft Research, 2001). These are studied cues, not a checklist that guarantees photorealism.
- Computational prediction. Fan et al. predict realism without a reference image. Other metrics compare image distributions. A review of synthetic image data cautions that common evaluation methods often focus on the synthesis model. They may not capture the quality of individual images or the diversity of a dataset (PLOS ONE review, 2022).
- Newer proposed metrics. A 2025 IEEE paper proposes the Global-Local Image Perceptual Score (GLIPS). Its authors say it aligns more closely with human evaluations than several conventional metrics in their study (IEEE, 2025). It is a proposed research metric, not a universally accepted benchmark.
What photorealistic does not mean
Photorealistic means visually convincing. It does not mean documentary, authentic or verified. In a 2022 PNAS study, Nightingale and Farid found that the AI-synthesized faces they evaluated could be indistinguishable from real faces to participants. They also discuss risks such as fraud and disinformation (PNAS, 2022). That finding is limited to their experiment. It does not extend automatically to every model, image type, viewer or current system.
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A practical definition therefore keeps three questions apart:
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- Does the image look like a photograph?
- How was it produced?
- Is the depicted event or person real?
Photorealism answers only the first.
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