gaming

Machine Learning used in remastering

2019-09-06 22:12 #0 by: jordan

Some very talented individuals have started to use machine learning to help in the remastering of old games!

While Gran Turismo 4 was among the most graphically impressive at the time of its launch, nowadays it is less impressive. Luckily, with the help of ESRGAN, it can be (to an extent) be brought back to its former glory, with an impressive 4K rendition of the games opening credits!

I wouldn't know if it is possible, but I would LOVE if this technology can continue to advance in the future, and allow us nostalgia seekers to experience the past in high quality!

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2019-09-09 10:38 #1 by: Niklas

Imagine that the original game was 640 x 448 pixels and played on desktop monitors. Not even our tiny phones have that low resolution nowadays. 😀

One thing I don’t understand, is how the AI remastering process is done. Does it improve anything that merely creating a modern version of the game would miss? They must add a lot of details that the original game never had.

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2019-09-09 15:22 #2 by: Leia

I wonder what will be the alternative to this in the next 20 years, will the kids of today even be nostalgia seekers? 

All the best, Leia

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2019-09-09 15:43 #3 by: jordan

#1 For this I imagine the differences would purely be graphical, leaving everything else as it was originally. Some might like that, as the quirks of the original remain, even if sometimes they were unintended glitches. 

As for how it is done, I found this. The abstract makes a little sense to me, but I can't pretend to understand the tech that was able to do this!

#2 I reckon they will be! We have already seen remasters of childhood games even from the past 15 years, such as 2007 Runescape and Classic World of Warcraft! I can definitely see a classic Fortnite with the original map being released to cater to the 'vets' of the game!

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2019-09-10 14:43 #4 by: Niklas

#2: I think nostalgia will always be around. Often, it is more about the feelings you remember than about a certain thing you had or did.

#3: I see. 🙂

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