Structural Omission is a framework for realist painting developed for the post-certainty era of generative AI, when images can be produced at scale with a surface of total certainty. This essay argues that realism remains viable only by abandoning completion as its premise. Traditional realism, even at its best, carried an old promise: that completion was available in principle, and that the artist could deliver wholeness if they chose. Generative AI systems now manufacture that kind of closure faster, cheaper, and more convincingly than any human hand. Structural Omission rebuilds realism on a different ground rooted in perception itself: perceptual limits are not an error in representation. They are the condition of representation. The framework formalizes planned, load-bearing incompleteness through three criteria—Ground (perceptual limits), Structure (structural incompleteness), and Consequence (narrative without resolution). Completion-biased image systems can imitate the look of omissions, but they usually treat gaps as a visual effect rather than structural absence. Rather than competing with algorithmic finish, Structural Omission treats partial visibility as the architecture holding the painting together. The result is realism that is accountable to human perception.
Deborah Scott (Thu,) studied this question.