An image generation method for unsupervised cross-domain image generation relative to a first image domain and a second image domain, the image generation method comprising: generating and storing a graph data structure in memory, the graph data structure comprising a plurality of connected nodes (23a, 23c, 23e, 23g, 23h, 23k, 231, 23o, 23p, 23q, 23u, 23v, 23w), the connected nodes (23a, 23c, 23e, 23g, 23h, 23k, 23l, 23o, 23p, 23q, 23u, 23v, 23w) comprising one or more object nodes (23a, 23c, 23e) representing components and one or more part characteristic nodes (23g, 23h, 23k, 231) representing part characteristics for each component, and one or more anomaly nodes (23o, 23p, 23q) representing potential anomalies for each part characteristic; receiving a 3D CAD (computer aided design) model comprising 3D model images of a target object; based on the 3D CAD model of the target object and the graph data structure, generating a plurality of augmented CAD models (32a-d) of the target object comprising a plurality of data sets (32a-d), each data set (32a-d) respectively corresponding to an associated one of a plurality of attribute classes, each data set (32a-d) comprising a plurality of 2D model images; inputting the plurality of data sets (32a-d) into a generative adversarial network comprising a plurality of generators (36a-d) respectively corresponding to the plurality of attribute classes and a plurality of discriminators (38a-d) respectively corresponding to the plurality of generators (36a-d); generating synthetic photorealistic images of the target object using the plurality of generators (36a-d), the synthetic photorealistic images including attributes in accordance with the plurality of data sets (32a-d) corresponding to the plurality of attribute classes; and outputting the synthetic photorealistic images of the target object, wherein the plurality of attribute classes includes an anomaly class; and the plurality of augmented CAD models (32a-d) is generated by: adding one or more anomalies into the 3D CAD model augmented based on the graph data structure; and rendering 2D model images of an anomaly class data set of the anomaly class using the augmented 3D CAD model. generating and storing a graph data structure in memory, the graph data structure comprising a plurality of connected nodes (23a, 23c, 23e, 23g, 23h, 23k, 231, 23o, 23p, 23q, 23u, 23v, 23w), the connected nodes (23a, 23c, 23e, 23g, 23h, 23k, 23l, 23o, 23p, 23q, 23u, 23v, 23w) comprising one or more object nodes (23a, 23c, 23e) representing components and one or more part characteristic nodes (23g, 23h, 23k, 231) representing part characteristics for each component, and one or more anomaly nodes (23o, 23p, 23q) representing potential anomalies for each part characteristic; receiving a 3D CAD (computer aided design) model comprising 3D model images of a target object; based on the 3D CAD model of the target object and the graph data structure, generating a plurality of augmented CAD models (32a-d) of the target object comprising a plurality of data sets (32a-d), each data set (32a-d) respectively corresponding to an associated one of a plurality of attribute classes, each data set (32a-d) comprising a plurality of 2D model images; inputting the plurality of data sets (32a-d) into a generative adversarial network comprising a plurality of generators (36a-d) respectively corresponding to the plurality of attribute classes and a plurality of discriminators (38a-d) respectively corresponding to the plurality of generators (36a-d); generating synthetic photorealistic images of the target object using the plurality of generators (36a-d), the synthetic photorealistic images including attributes in accordance with the plurality of data sets (32a-d) corresponding to the plurality of attribute classes; and outputting the synthetic photorealistic images of the target object, wherein the plurality of attribute classes includes an anomaly class; and the plurality of augmented CAD models (32a-d) is generated by: adding one or more anomalies into the 3D CAD model augmented based on the graph data structure; and rendering 2D model images of an anomaly class data set of the anomaly class using the augmented 3D CAD model. adding one or more anomalies into the 3D CAD model augmented based on the graph data structure; and rendering 2D model images of an anomaly class data set of the anomaly class using the augmented 3D CAD model.