Research on Image Style Transfer Based on a Generative Model
DOI:
https://doi.org/10.61173/2hp2z214Keywords:
Generative AI, Sora, Image Generation, GAN, Image style transferAbstract
The generation of artificial intelligence technology is rapidly developing. As a representative, Sora created in text, image rendering, and video production shows excellent ability. This article discusses the image-style transmission algorithm based on generating a confrontation network (GAN) and compares it with the Sora model. Studies have shown that GAN performs well in image style transfer, especially in maintaining the consistency of the image content and generating high-quality images. In the future, the expected artificial intelligence technology will further improve the quality and diversity of generating content by combining the diffusion model and GAN and expanding its application potential in the fields of art creation and virtual reality.
References
[1] ZHU J-Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C] // Proceedings of the IEEE International Conference on Computer Vision. 2017: 2223 – 2232.
[2] ZHENG C, CHAM T-J, CAI J. The spatially-correlative loss for various image translation tasks[C] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 16407 – 16417.
[3] LEE J. Diffusion rendering of black ink paintings using new paper and ink models[J].Computers & Graphics, 2001, 25(2) : 295 – 308.
[4] ZHANG H,XUT,LIHS, et al.StackGAN: text to photorcalistic image synthesis with stacked generative adversarial networks[R]. Arxiv Preprint Arxiv:1612.03242, 2016.
[5] LEONG,ALEXANDER E,MATTHIAS B. A neural algorithmof artistic style[R]. Arxiv Preprint Arxiv:1508.06576, 2015b.
[6] HUANGG,CHEN DL,LlT H,et al,Multiscale dense net-works for resource efficient immage classification[C].ICLR,2018.
[7] MIRZA M, OSINDERO S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014
[8] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative Adversarial Nets[C] // NIPS. 2014.
[9] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
[10] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11) : 2278 – 2324.
[11] XUE A. End-to-end chinese landscape painting creation using generative adversarial networks[C] // Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021 : 3863 – 3871.
[12] ZHANG F, GAO H, LAI Y. Detail-preserving CycleGAN- AdaIN framework for image-to-ink painting translation[J]. IEEE Access, 2020, 8 : 132002 – 132011.
[13] YU J, LUO G, PENG Q. Image-based synthesis of Chinese landscape painting[J].Journal of Computer Science and Technology, 2003, 18(1) : 22 – 28.
[14] ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial networks[C] // International conference on machine learning. 2017 : 214 – 223.
[15] ARJOVSKY M, BOTTOU L. Towards Principled Methods for Training Generative Adversarial =Networks[J]. arXiv preprint arXiv:1701.04862, 2017.
[16] ISOLA P, ZHU J-Y, ZHOU T, et al. Image-to-image translation with conditional adversarial networks[C] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017 : 1125 – 1134.
[17] HEUSEL M, RAMSAUER H, UNTERTHINER T, et al. Gans trained by a two timescale update rule converge to a local nash equilibrium[J]. Advances in Neural Information Processing Systems(NIPS), 2017, 30.
[18] LIU Y. Improved generative adversarial network and its application in image oil painting style transfer[J]. Image and Vision Computing, 2021, 105 : 104087.
[19] CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine intelligence, 1986(6) : 679 – 698.
[20] YAN M, WANG J, SHEN Y, et al. A non-photorealistic rendering method based on Chinese ink and wash painting style for 3D mountain models[J]. Heritage Science, 2022, 10(1) : 1 – 15.
[21] XIE S, TU Z. Holistically-nested edge detection[C] // Proceedings of the IEEE International Conference on Computer Vision. 2015 : 1395 – 1403.
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