Imagine the joy of uncovering an old videotape, even just your family home video. But suddenly I have to sit down and watch a two-hour-long recording that sounds bad and unclear. Adobe has now launched a remarkable new project to make the most memorable moments of that old photo of my parents shine again. Recently, VideoGigaGAN was one of the AIs that can increase the resolution of videos by eight times without blurring, making them as clear and vivid as real videos.
VideoGigaGAN’s work has been recently posted in a research paper and it is counted as a super VSR model that goes beyond the existing standards. VideoGigaGAN differs from earlier methods in that it can introduce a variety of flicker-like artifacts by making attention to detail the main priority without sacrificing realism. This success is mainly due to the use of Generative Adversarial Networks (GAN) – one of the best technologies when it comes to advanced still-photos. Despite this, using GANs on video remains challenging; Sometimes inconsistencies occur between frames.
However, VideoGigaGAN is facing a hurdle with the exciting dance of these two artificial intelligence systems. One creates a highly detailed and accurate version while the other is like a gatekeeper that sorts the output into a lookup table as exceptions occur. This iterative method guarantees the result of a video through beautiful images at the same time.
Adobe offers clip demos that show what a person or company can do with these tools. In some details, for example, skin texture, computer algorithms can understand them, but, at the same time, the general result will remain real. The merging of AI-manipulated scenes and the real world is testing us with a thought-provoking question – with up-to-date equipment, can we tell authentic videos from their AI-generated counterparts in the future?
Currently, we look at a cutting-edge research possibility, VideoGigaGAN, which shows how poor memories that lack high definition can be retrieved in spectacular detail. Similarly, this technology is a reflection of Adobe’s previous successful use of machine learning to make photo editing more easy through Project Race-Up. Still, the competition for the position of king of VSRs is far from over, with companies like Microsoft and Nvidia also leaving no stone unturned in their bid to claim the top spot. As this field continues to evolve, one thing remains certain: explicit videos may just be a thing of history shortly that can help overcome this.
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