论文标题
通过残留的快速傅立叶变换改善人类图像的合成和瓦斯汀距离
Improving Human Image Synthesis with Residual Fast Fourier Transformation and Wasserstein Distance
论文作者
论文摘要
随着元元的快速发展，虚拟人类已经出现，人类形象的综合和编辑技术（例如姿势转移）最近变得流行。大多数现有的技术都依赖于gan，即使有大型变体和遮挡也可以产生良好的人类图像。但是据我们所知，现有的最新方法仍然存在以下问题：首先是合成图像的渲染效果是不现实的，例如某些地区的渲染不佳。第二个是GAN的训练不稳定且融合缓慢，例如模型崩溃。基于以上两个问题，我们提出了几种解决这些问题的方法。为了提高渲染效果，我们使用残留的快速傅立叶变换块来替换传统的残留块。然后，光谱归一化和瓦斯坦距离用于提高gan训练的速度和稳定性。实验表明，我们提供的方法可有效解决上述问题，并且我们在LPIPS和PSNR中获得最先进的分数。
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.
