AI News: Animating Heads With One-Shot Learning
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by UtopiaNemo
5 hours ago

I have the utmost respect for each of these developers. I must say I think they’re mostly incorrect in their assessments of why the Dreamcast failed. The Dreamcast’s ultimate failure had so little to do with the way Sega handled the Dreamcast. Sega and their third party affiliates such as Namco and Capcom put out so many games of such stellar quality, that the Dreamcast won over a generation of gamers who had previously been diehard Nintendo or Sony fans. They even won me over, who had been a diehard Sega fan since the SMS days, but was so disillusioned by the Saturn’s handling that I had initially decided to sit the Dreamcast out. At that time, the Dreamcast launch was widely considered to be the strongest console launch in US history. In my opinion, the three issues leading to the fall of the Dreamcast were (in inverse order):1)piracy, 2)Sega’s great deficit of finances and cachet following the Saturn debacle, and 3)Sony’s masterful marketing of the PlayStation 2. Piracy’s effect on Dreamcast sales is a hotly debated topic, but I’ll say that the turn of the millennium, most college and post-college guys I knew pirated every bit of music or software they could. Regarding the Saturn debacle, the infighting between SOA and SOJ is well known, as are the number of hubristic decisions Mr. Nakayama made which left Sega in huge financial deficit. They were also directly responsible for erasing a lot of the respect and good will Sega had chiseled out worldwide during the Mega Drive/Genesis era. With the Dreamcast, Sega was digging itself out of a hole. They had seemingly done it as well, and would have surely continued along that path, had it not been for the PS2. There is no doubt in my mind that the overwhelming reason the Dreamcast failed was because of the PS2.

by Philip Ho
8 hours ago

Great stuff Fran!

What the hell are you saying? I can't make sense of it.

AI News: Animating Heads With One-Shot Learning
24 May, 2019

Researchers from Samsung AI Center revealed their new AI system that allows animating heads using only a few static shots.  The team’s paper, Few-Shot Adversarial Learning of Realistic Neural Talking Head Models, discusses the system that performs lengthy meta-learning on a large dataset of videos, and further frames few and one-shot learning of neural talking head models of previously unseen people with the help of high capacity generators and discriminators.

The system is said to initialize the parameters of both the generator and the discriminator in a person-specific way so the training can be based on just a few images and can be done quickly. It all means that the new approach is capable of learning highly realistic and personalized talking head models of new people and even portrait paintings.

Their aim here was to synthesize video-sequences of speech expressions and mimics of a particular individual.

They’ve studied the problem of synthesizing photorealistic personalized head images with a set of face landmarks to drive the animation of the model. It will be useful for video conferencing, multi-player games, VFX, and more.

You can learn more about the system here.

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