StyleGAN3: Generative Networks for Improving the Equivariance of Translation and Rotation

Gaurav Gupta; Junqi Chen; Renzhe Zhu; Tianlei Zhu

StyleGAN3: Generative Networks for Improving the Equivariance of Translation and Rotation

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arxiv 2307.03898 v3 pith:2ITL6HFS submitted 2023-07-08 cs.CV eess.IV

Tianlei Zhu , Junqi Chen , Renzhe Zhu , Gaurav Gupta This is my paper · ORCID

classification cs.CVeess.IV

keywords stylegan3affectdatasetequivariancegenerativestyleganusedversions

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StyleGAN can use style to affect facial posture and identity features, and noise to affect hair, wrinkles, skin color and other details. Among these, the outcomes of the picture processing will vary slightly between different versions of styleGAN. As a result, the comparison of performance differences between styleGAN2 and the two modified versions of styleGAN3 will be the main focus of this study. We used the FFHQ dataset as the dataset and FID, EQ-T, and EQ-R were used to be the assessment of the model. In the end, we discovered that Stylegan3 version is a better generative network to improve the equivariance. Our findings have a positive impact on the creation of animation and videos.

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