Generative adversarial network framework for Chinese literati painting generation driven by artistic conception theory

Chinese literati painting resists computational treatment for a reason that is easy to state and hard to model: its aesthetic value rests less on the objects depicted than on artistic conception. This paper takes on one well-defined slice of that problem, namely synthesising the painted background scene and then coordinating it with the remaining three perfections. Our system couples a generative adversarial network (GAN) with classical aesthetic theory. The generator produces background scenes only, while a surrounding pipeline assembles the complete four-perfections composition; we keep the two roles apart throughout the paper so that background synthesis is never mistaken for whole-painting generation. The conception-aware GAN recasts emptiness-substance dialectics, spatial rhythm, and atmospheric mood as explicit computational constraints. A Transformer-based cross-modal model then links poetry, calligraphy, painting, and seal impressions through knowledge-guided mapping and attention, and an assistance subsystem supplies tiered decision support while leaving creative authority with the artist. We trained and evaluated the model on a curated corpus of 12,486 literati painting backgrounds against six baselines, among them Stable Diffusion v2.1 and DALL-E 3. Four of these systems, namely the standard GAN, CycleGAN, StyleGAN2, and our own ablation, were trained by us under matched settings and therefore support a controlled comparison; the two diffusion baselines differ in scale, training data, and access, so we report those contrasts as indicative rather than as head-to-head tests. Across ten independent runs (paired t-test with Bonferroni correction), Fréchet Inception Distance falls from 41.5 to 31.2 and Inception Score rises from 4.78 to 5.34, and the widest separation appears on a domain-specific conception measure rather than on perceptual metrics alone. A 42-participant user study reports favourable ratings across nine usability dimensions, although the sample is modest and drawn from a single cultural setting. The contribution is therefore twofold: a route to formalising culturally specific aesthetics inside a neural framework, and an explicit account of what the present evidence does and does not establish.