Draw the outline of a shoe and ask a program to fill it in. The outline establishes a shape, but it leaves nearly everything else open: material, color, stitching, lighting and texture. A system can produce something plausible without recovering a uniquely correct answer, because the outline never contained a uniquely correct answer.
That makes edges-to-images a useful way into pix2pix. The November paper by Isola, Zhu, Zhou and Efros studies image-to-image translation using conditional adversarial networks. The condition is another image, such as an edge map or a set of region labels, rather than a free-form instruction.
A pair defines the task
Training examples establish a relationship between an input and an expected output. A map paired with an aerial image describes one relationship. An edge image paired with a photograph describes another. The general framework can be reused, but the data still defines what the model is being asked to learn.
The generator attempts the translation. A discriminator learns to distinguish real pairs from pairs containing generated output. Comparing pixels encourages agreement with the target, while the adversarial objective pushes the result toward characteristics that make a pair look plausible.
Those pressures are useful for different reasons. If several detailed answers would fit the same coarse input, averaging them can blur away the details. A learned judgment about local appearance offers another signal. It does not guarantee that the chosen detail is factually true.
Plausibility needs a label
Imagine using a rough building outline as input. A generated facade might contain windows that look convincing and are entirely invented. For a concept sketch, that may be desirable. For documenting an existing building, it would be a serious change in the meaning of the image.
The interface should make that distinction easy to retain. Keep the input visible. Let the user compare alternatives. Avoid presenting the output as a recovered photograph when it is a proposed completion constrained by learned examples.
I am especially interested in translation tasks with an intentionally limited vocabulary. A drawing interface for a particular kind of object can make its constraints visible. The user learns which marks matter, and the system's behavior becomes something they can steer instead of something they can only admire after pressing a button.
That also suggests a way to inspect failures. Give the system an unusual outline, an incomplete boundary or a combination poorly represented in its training data. Does it preserve the instruction, ignore part of it, or substitute a familiar shape? The answer reveals the relationship it learned more clearly than another polished result.
pix2pix makes the specification of an image feel more direct: provide an image describing one aspect and ask for another representation. The remaining work is deciding which aspects must be preserved exactly and where invention is welcome. That is a design decision the network cannot make on the user's behalf.