A learning method for learning a transformation of one or more real images on a real world into one or more virtual images on a virtual world by using a cycle Generative Adversarial Network, GAN, comprising steps of: (a) after acquiring at least one first image, I, which is one of the real images, (i) instructing, by a learning device, a first transformer to transform the first image to at least one second image, G, whose one or more characteristics are same as or similar to those of the virtual images, (ii-1) instructing, by the learning device, a first discriminator to determine whether the second image is one of primary virtual images or one of secondary virtual images, wherein the primary virtual images are at least part of the virtual images without a transformation from at least part of the real images, and the secondary virtual images are at least part of the virtual images with the transformation from at least part of the real images, to thereby generate a 1_1 - st result, D G ( G)), and (ii-2) instructing, by the learning device, a second transformer to transform the second image to at least one third image, F ( G), whose one or more characteristics are same as or similar to those of the real images; (b) after acquiring at least one fourth image, X, which is one of the virtual images, (i) instructing, by the learning device, the second transformer to transform the fourth image to at least one fifth image, F, whose one or more characteristics are same as or similar to those of the real images, (ii-1) instructing, by the learning device, a second discriminator to determine whether the fifth image is one of primary real images or one of secondary real images, wherein the primary real images are at least part of the real images without a transformation from at least part of the virtual images, and the secondary real images are at least part of the real images with the transformation from at least part of the virtual images, to thereby generate a 2_1-st result, D F ( F), and (ii-2) instructing, by the learning device, the first transformer to transform the fifth image to at least one sixth image, G ( F), whose one or more characteristics are same as or similar to those of the virtual images; and (c) calculating, by the learning device, one or more losses by referring to at least part of the first image, the second image, the third image, the fourth image, the fifth image, the sixth image, the 1_1-st result, and the 2_1 - st result, to thereby learn at least part of parameters of the first transformer, the second transformer, the first discriminator, and the second discriminator, characterized in that, at the step of (c), after acquiring (i) a virtual object detection result, OD(G), OD(G(F)), on at least part of the second image and the sixth image generated by a virtual object detector, which detects at least one virtual object included in its inputted image, and (ii) its corresponding ground-truth, instructing, by the learning device, a loss unit to generate a transformer loss included in said one or more losses, by further referring to the virtual object detection result. (a) after acquiring at least one first image, I, which is one of the real images, (i) instructing, by a learning device, a first transformer to transform the first image to at least one second image, G, whose one or more characteristics are same as or similar to those of the virtual images, (ii-1) instructing, by the learning device, a first discriminator to determine whether the second image is one of primary virtual images or one of secondary virtual images, wherein the primary virtual images are at least part of the virtual images without a transformation from at least part of the real images, and the secondary virtual images are at least part of the virtual images with the transformation from at least part of the real images, to thereby generate a 1_1 - st result, D G ( G)), and (ii-2) instructing, by the learning device, a second transformer to transform the second image to at least one third image, F ( G), whose one or more characteristics are same as or similar to those of the real images; (b) after acquiring at least one fourth image, X, which is one of the virtual images, (i) instructing, by the learning device, the second transformer to transform the fourth image to at least one fifth image, F, whose one or more characteristics are same as or similar to those of the real images, (ii-1) instructing, by the learning device, a second discriminator to determine whether the fifth image is one of primary real images or one of secondary real images, wherein the primary real images are at least part of the real images without a transformation from at least part of the virtual images, and the secondary real images are at least part of the real images with the transformation from at least part of the virtual images, to thereby generate a 2_1-st result, D F ( F), and (ii-2) instructing, by the learning device, the first transformer to transform the fifth image to at least one sixth image, G ( F), whose one or more characteristics are same as or similar to those of the virtual images; and (c) calculating, by the learning device, one or more losses by referring to at least part of the first image, the second image, the third image, the fourth image, the fifth image, the sixth image, the 1_1-st result, and the 2_1 - st result, to thereby learn at least part of parameters of the first transformer, the second transformer, the first discriminator, and the second discriminator,