Human video synthesis using generative adversarial networks

In this work, a video synthesis model based on Generative Adversarial Networks (Human GAN) is proposed, whose objective is to generate a photorealistic output by learning the mapping function from an input source to output video. However, the image to image generation is a quite popular problem, but the video synthesis problem is still unexplored. Directly employing existing image generation method without taking temporal dynamics into account leads to frequent temporally incoherent output with low visual quality. The proposed approach solves this problem by wisely designing generators and discriminators combined with Spatio-temporal adversarial objects. While comparing it to some robust baselines on public benchmarks, the proposed model proves to be superior in generating temporally coherent videos with extremely low artifacts. And results achieved by the proposed model are more realistic on both quantitative and qualitative measures compared to other existing baselines techniques.