Open Access

Research on Visual Design of AI Painting Based on SolidWorks Technology


Cite

Williams, B. (2021). Painting by numbers: copyright protection and ai-generated art. European intellectual property review(12), 43. Search in Google Scholar

Yue, P., & Yuan, T. (2022). Artificial intelligence-assisted interior layout design of cad painting. Computer-Aided Design and Applications. Search in Google Scholar

Marcelo Fraile-Narváez, Sagredo-Olivenza, I., & Mcgowan, N. (2022). Painting authorship and forgery detection challenges with ai image generation algorithms: rembrandt and 17th century dutch painters as a case study. Int. J. Interact. Multim. Artif. Intell., 7, 7. Search in Google Scholar

Pavithra, V., Rosy, S., Srinishanthini, R. B., & Prinslin, L. (2023). Text-to-image generation using ai. International Journal of Research Publication and Reviews. Search in Google Scholar

Guo, X., Lu, X., Lin, Q., Zhang, J., Hu, X., & Che, S. (2022). A novel retinal image generation model with the preservation of structural similarity and high resolution. Biomed. Signal Process. Control., 78, 104004. Search in Google Scholar

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2023). Pose-driven attention-guided image generation for person re-identification. Pattern Recognition: The Journal of the Pattern Recognition Society. Search in Google Scholar

Souza, V. L. T. D., Marques, B., Batagelo, H. C., & Gois, J. P. (2023). A review on generative adversarial networks for image generation. Computers & Graphics. Search in Google Scholar

Pan, Z., Zhou, X., & Tian, H. (2022). Arbitrary style guidance for enhanced diffusion-based text-to-image generation. arXiv e-prints. Search in Google Scholar

Wang, J. Y., Zhang, C. G., & Yang, H. T. (2023). Improved singan for single-sample airport runway destruction image generation. Recent advances in computer science and communications. Search in Google Scholar

Rutter, R. N., Barnes, S. J., Roper, S., Nadeau, J., & Lettice, F. (2021). Social media influencers, product placement and network engagement: using ai image analysis to empirically test relationships. Industrial management & data systems(12), 121. Search in Google Scholar

Durham, G., & Chisek, J. (2023). Text to image ai generators: familiar legal questions about this novel and fast-growing technology. The computer & internet lawyer. Search in Google Scholar

Lu, Y., Hou, R., & Zheng, J. (2023). A context-aware image generation method for assisted design of movie posters using generative adversarial network. Journal of Circuits, Systems and Computers, 32(13). Search in Google Scholar

Kim, J., Kim, M., Shin, Y. G., & Chung, M. (2023). Accurate depth image generation via overfit training of point cloud registration using local frame sets. Computer vision and image understanding: CVIU. Search in Google Scholar

Chen, X., Cohen-Or, D., Chen, B., & Mitra, N. J. (2021). Towards a neural graphics pipeline for controllable image generation. John Wiley & Sons, Ltd(2). Search in Google Scholar

Rudakov, I. V., Filippov, M. V., & Kudryavtsev, M. A. (2023). Image generation method based on the recoverable byte sequence using the neural networks. Herald of the Bauman Moscow State Technical University. Series Instrument Engineering. Search in Google Scholar

Bintoro, C., Wuwung, V., & Suradi, R. (2023). Design and construction of mountain bike frame using solidworks software and matlab simulink. American Journal of Mechanical and Materials Engineering. Search in Google Scholar

eISSN:
2444-8656
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics