Glossary
What is Route Map?
A route map is a diagnosis-to-technique framework for AI product photography that starts by naming exactly what is wrong with a generated image, using eight fixed accuracy axes, silhouette, proportions, element count, text, graphics, material, color, and construction details, and then selects the one technique built to fix that specific defect. The premise is that many different techniques can reach the same accurate result, and choosing the right one for the diagnosis is itself the skill; jumping randomly between techniques wastes generations, while repeating the same failing technique harder rarely helps. The discipline is diagnose first, pick one technique, commit to it, and only switch methods when that specific technique keeps failing rather than switching at random.
Understanding Route Map.
The starting premise is that many different techniques can reach the same accurate result, so choosing which one to use is itself the diagnostic skill, not an afterthought. A route map begins by naming what's wrong using a fixed set of eight accuracy axes: silhouette and outline, proportions and scale, element count, text and typography, graphics and pattern, material and finish, color accuracy, and construction details. The axes stay fixed across every product; only the specific features being checked change.
Once the defect is named, the map dispatches to the technique built for that specific problem. A product needing a new angle the source photo doesn't show gets pose-matching. A size or proportion problem gets dimension control. A material that reads wrong gets material fidelity work. A mystery defect that survives every attempted fix gets Shannon descent. A scene needing to be rebuilt around one replaced element gets blueprinting.
The discipline this enforces is against technique-hopping: diagnose first, pick one technique, commit to it, and only switch to a different technique once that specific one has genuinely failed, rather than jumping between methods at random and hoping one works.
How It Relates to AI Photography.
Route map thinking is implemented live in Dezygn's fidelity judge, which runs every generated image through the same eight-axis check and routes flagged defects to the technique built to fix that exact axis, turning what used to be an experienced photographer's intuition into a repeatable, teachable process. Full technique breakdown at /resources/product-accuracy-route-map.
Related Terms.
Count Error
A count error is a product-accuracy defect where an AI-generated image shows the wrong number of a product's repeating parts, buttons, straps, legs, or stitches, even when the rest of the product looks correct.
Shannon Descent
Shannon descent is an AI image troubleshooting technique that shrinks a failing composite down to its smallest failing piece, perfects that piece alone, then rebuilds the full scene back around the solved element, checking that it survives each step.
Lock-and-Outpaint
Lock-and-outpaint is an AI product photography technique that keeps the product image pixel-locked in its exact original position, crop, and angle while the AI generates only the environment around it.
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