![]() On top of this, it employs a progressive-growth approach that starts with first perfecting lower resolution outputs and using that knowledge it progressively moves up to produce higher resolution output. This ensures that there is very little flickering between the frames. ![]() ![]() On the discriminator side, it uses an I mage Discriminator to control the quality of output and in additon to that, it also uses a Video Dicriminator to ensure that the frame-by-frame sequence of the synthesized images makes sense according to the flow maps. ![]() This provides the information required to understand the difference between two consecutive frames and is therefore able to synthesize temporally consistent images. Additionally, it uses the final synthesized frames from its previous output and combines these together to compute a F low Map. ![]() The vid2vid network’s Generator uses not only the current semantic map that we want to convert, but also few semantic maps from the previous frames.
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