StyleGAN3: Generative Networks for Improving the Equivariance of Translation and Rotation
This incremental improvement in generative networks could benefit animation and video creation by enhancing image consistency.
The study compared StyleGAN2 with two modified versions of StyleGAN3 on the FFHQ dataset, finding that StyleGAN3 is better at improving equivariance in translation and rotation, as measured by FID, EQ-T, and EQ-R metrics.
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.