Revealing the Secrets of Creating New Game Characters Without Using Paintbrush

  • Instability in training-Mode collapse is an inherent problem in the GAN generator, which occurs when the generator doesn’t learn any useful representation which is indistinguishable from the real images, thus making the discriminator always win.
  • Capable of generating relatively smaller images, such as 64*64 pixels.
  • These limitations are overcome by using a more sophisticated model, known as Progressive Growing GAN (ProGAN), specifically designed to minimize the limitations of vanilla GANs.
Fig 1. A- Random generation of MK characters using Random Noise vector as input, B-Controlled generation of MK characters using Random Noise vector and labeled data as input (Condition-Create MK characters with features as Male, Beard, Moustache)

Architectural details of StyleGANs

a) ProGAN generator b) StyleGAN generator
  1. ‘config-a’, # Baseline Progressive GANs

Experimental setup

Fig 3. Sample images from the training MK dataset

Experimental design

Evaluation metrics

Experimental platform

Experiments results

Fig 4. MK training results 1-Kims 2364, 2-Kims 5306 , 3-Kims 6126, 4-,Kims 8006 5-Kims 9766, 6- 10000 Kims
Table 3. Time taken for training & FID score
Fig 5. Real-time images generation (using seed value) @7726 kims pickle MK.
Fig 6. Style Mixing results on MK characters
Fig 7. Progressive GANs output

Conclusion

About Affine

References

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Affine is a provider of analytics solutions, working with global organizations solving their strategic and day to day business problems www.affineanalytics.com

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Affine

Affine

Affine is a provider of analytics solutions, working with global organizations solving their strategic and day to day business problems www.affineanalytics.com

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