  With the rapid development of the Metaverse, virtual humans have emerged, and
human image synthesis and editing techniques, such as pose transfer, have
recently become popular. Most of the existing techniques rely on GANs, which
can generate good human images even with large variants and occlusions. But
from our best knowledge, the existing state-of-the-art method still has the
following problems: the first is that the rendering effect of the synthetic
image is not realistic, such as poor rendering of some regions. And the second
is that the training of GAN is unstable and slow to converge, such as model
collapse. Based on the above two problems, we propose several methods to solve
them. To improve the rendering effect, we use the Residual Fast Fourier
Transform Block to replace the traditional Residual Block. Then, spectral
normalization and Wasserstein distance are used to improve the speed and
stability of GAN training. Experiments demonstrate that the methods we offer
are effective at solving the problems listed above, and we get state-of-the-art
scores in LPIPS and PSNR.
