Visual Semantics of Ai-Generated Paintings
Bibliographic Data
| ID | 22196229 |
|---|---|
| Authors | Arvind Kumar Pandey (0000-0001-5294-0190, National Institute of Technology Jamshedpur), Lakshya Swarup (0009-0006-3351-5248, Chitkara University), Sangeet Saroha (Noida International University), Harsha Gupta (Jaypee Institute of Information Technology), Vibhor Mahajan (Chitkara University), Sandhya Damodar Pandao (MIT Art, Design and Technology University) |
| Year | 2025 |
| Volume | 6 |
| Issue | 3s |
| Pages | 62-71 |
| Publication date | 2025-12-20 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ShodhKosh: Journal of Visual and Performing Arts (JOURNAL) |
| Journal identifiers | ISSN: 2582-7472 • E-ISSN: 2582-7472 |
| Publisher | Granthaalayah Publications and Printers (PUBLISHER • IN) |
| DOI | 10.29121/shodhkosh.v6.i3s.2025.6801 |
| OpenAlex | W7117248175 |
| Language | EN |
| References cited | 17 |
This paper examines the visual semantics of AI generated paintings focusing on the crossroads between the computational creativity and the aesthetic interpretation of paintings by humans. With the rise of artificial intelligence based on powerful image-generation models, including Generative Adversarial Networks (GANs) and diffusion models, the ability of machines to generate image-based artworks with visual complexity and symbolic richness has increased. Nonetheless, it is a question to whether these visual products contain actual semantic depth or they only imitate human artistic intent. The study is based on a mixed-method design that involves a combination of both computational image analysis and qualitative semantic interpretation in order to explore the construction and perception of meaning within AI-generated art. The results of analysis of a curated dataset of AI-generated paintings were presented using CLIP, DALL•E, and Midjourney to obtain visual features and project them on conceptual and emotional planes. By applying the conceptual theories of semiotics and aesthetics, the paper determines the trends in color, composition and symbolism that are used to encode cultural and perceptual information in AI models. It has been found that though AI systems are able to adopt a human-like semantics via visual correlations learned by training, their results are essentially derivational: based on training data and probability associations as opposed to capturing original creative intent. The discourse explains the ways AI-generated paintings question traditional limits of authorship and artistic meaning in that they propose a new paradigm, in which human users collaborate to create semantics out of algorithmic aesthetics
Computational Creativity · Computational semantics · Creativity · Painting · Perception · Semiotics · Aesthetic Perception and Analysis · Generative Adversarial Networks and Image Synthesis · Music Technology and Sound Studies
| Citation velocity | historical |
|---|---|
| Highly cited | No |