基于时空生成对抗网络的视频修复

The existing video inpainting methods may fail to yield semantic continuous results. We proposed a method based on temporal-spatial generative adversarial networks to solve the above problem. This method includes two network models: the single-frame inpainting model and the sequence inpainting model. The single-frame inpainting model consisting of the single-frame stacked generator and spatial discriminator can realize the high-quality completion for the start frames with spatial missing regions. On this basis, the sequence inpainting model consisting of the sequence stacked generator and the temporal-spatial discriminator is used to achieve the temporal-spatial consistent video completion for the subsequent frames. Experimental results on the UCF-101 and FaceForensics datasets show that our method can greatly improve the temporal and spatial coherence of video completion. Compared with the benchmark method, our method performs better in peak signal to noise ratio, structural similarity index, learned perceptual image patch similarity and stability error.