RealSynz: Naturalizing Synthetic Images with Envelope Variation Statistics

Generated images still show consistent shortcomings and artifacts compared with natural images. While they represent scenes well, textures such as unnaturally waxy skin and flat fabric place them in an uncanny valley. We find that the difference lies in how fine detail is distributed. Three of the five generators we study carry less fine detail than natural images, and the two that carry as much or more distribute it differently. In textured regions, every generator packs fine detail into small areas and leaves the space between them nearly empty. In flat regions, three of the five generators spread fine detail too evenly. We measure this distribution with two statistics of natural images. The moment ratio is the mean absolute value of fine detail in a patch divided by its root mean square, and envelope variation is the coefficient of variation of the local magnitude of fine detail across a patch. Edits that change only the amount of detail leave both statistics far from natural images. Based on this finding, we present RealSynz, an independent module that corrects this distribution in the output of any generator without access to the model. RealSynz corrects the moment ratio in closed form and trains a small network that decides only where to redistribute fine detail and where to add texture. On 1,000 anachronistic FLUX.2 Klein scenes, RealSynz closes 65% of the envelope variation gap, outperforms fifteen baseline methods, and transfers to four other generators without retraining.