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Deep Learning-based Research on Stylistic Migration and Creative Assistance for Drawing Artworks

Chao Jiang

Chao Jiang

Faculty of Arts Education, Hubei Institute of Fine ArtsWuhan, China
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Jiang, Chao
 und 
Manqiu Xu

Manqiu Xu

Faculty of Digital Media, College of International Business and Economics, WTUWuhan, China
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Xu, Manqiu
  
19. März 2025
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Online veröffentlicht: 19. März 2025
Eingereicht: 31. Okt. 2024
Akzeptiert: 13. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0495
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© 2025 Chao Jiang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

CycleGAN network architecture
CycleGAN network architecture

Figure 2.

Generator architecture
Generator architecture

Figure 3.

Discriminator architecture
Discriminator architecture

Figure 4.

Loss comparison
Loss comparison

Evaluation results

SSIM Rubens Dürer Zorn Menzel Seurat
DualGAN 0.444 0.513 0.191 0.471 0.377
CGGAN 0.433 0.517 0.172 0.458 0.226
StyleGAN 0.474 0.545 0.128 0.398 0.244
AdaIN 0.464 0.552 0.193 0.451 0.371
AdaAttIN 0.478 0.571 0.243 0.405 0.298
IEST 0.452 0.487 0.182 0.431 0.238
LapStyle 0.419 0.503 0.136 0.367 0.355
SANet 0.434 0.486 0.254 0.472 0.281
CCPL 0.409 0.524 0.216 0.401 0.346
SSTR 0.418 0.556 0.214 0.451 0.358
ArtFlow 0.485 0.495 0.242 0.409 0.384
Ours 0.498 0.576 0.257 0.484 0.392
PSNR Rubens Dürer Zorn Menzel Seurat
DualGAN 11.075 12.213 8.684 10.956 10.288
CGGAN 10.863 12.504 7.971 10.406 10.608
StyleGAN 12.343 14.297 7.707 11.523 10.346
AdaIN 12.424 14.153 8.991 12.178 9.584
AdaAttIN 10.507 12.897 8.179 11.574 10.471
IEST 13.572 13.591 8.999 10.275 9.634
LapStyle 10.772 13.091 8.224 10.393 10.974
SANet 13.277 13.841 7.662 13.092 9.917
CCPL 13.108 11.071 8.367 11.243 9.308
SSTR 11.381 13.022 8.426 12.418 10.649
ArtFlow 10.866 13.301 8.027 10.567 9.531
Ours 13.808 14.573 9.087 13.607 11.001

Model size and speed comparison

Algorithm Parameter quantity Style migration time (s)
1000×750 750×562 500×375
DualGAN 20.68M 101.66 96.40 88.47
CGGAN 16.92M 55.78 44.61 42.79
StyleGAN 14.55M 90.33 61.15 50.73
AdaIN 8.86M 23.49 22.06 17.95
AdaAttIN 9.04M 156.85 74.74 51.66
IEST 12.01M 14.79 13.16 7.76
LapStyle 12.47M 98.51 94.98 36.83
SANet 20.75M 119.05 116.25 33.49
CCPL 5.69M 11.42 10.46 8.28
SSTR 13.37M 23.29 16.97 12.12
ArtFlow 8.63 M 7.98 6.05 5.55
Ours 1.02 M 3.42 2.19 1.72

The results of the user study

Algorithm Content quality (%) Style strength (%) Fondness (%)
DualGAN 1.34 2.31 3.79
CGGAN 3.72 2.17 3.59
StyleGAN 4.88 1.33 2.13
AdaIN 1.01 3.04 4.33
AdaAttIN 3.37 3.87 1.81
IEST 2.59 4.28 0.96
LapStyle 3.14 5.84 6.13
SANet 4.69 2.88 4.75
CCPL 3.66 3.89 5.41
SSTR 2.94 0.99 4.75
ArtFlow 4.08 3.67 2.26
Ours 64.58 65.73 60.09
Sprache:
Englisch
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Fachgebiete der Zeitschrift:
Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere
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