FIFA 22 - The direction of EA was limited to giving the players
FIFA 22 - The direction of EA was limited to giving the players has not posted anything yet
The game changer developed by EA was a local positioning system. Instead of GPS satellites LPS beacons were distributed all over the field, along with motion sensors attached to players' chests.
It meant high-quality data could finally be captured in a real football match. "We picked the two teams in Spain's third tier because they have an historic clash," Eaves says. "They're both professional, well-coached teams with a fantastic technique. They play possession-based football with control and finesse . They're perfect for a video game as we need it to be quick and enjoyable."
The direction of EA was limited to giving the players and coaches employees that it was an "must-win game." The game needed an extra edge. "Just like in a regular game, players were subbed on and off, we could get tired defenders out and fresh forwards when the game began to unfold," recalls Eaves. "You could feel the animosity. There were so many fouls within the first five minutes , it was exactly what we needed."
Off-the-ball movements like shouting, pointingand posturing - were priceless for EA in capture of the intensity and the physicality of a real-life sport. "If an action-sports stuntman slides over one in a motion capture environment and then they pop back up, but it does not have the same intensity as an actual challenge," explains Eaves. "In this game physical challenge, there is a real. The player stands up and is angry, we take all that energy and apply it to the game."
It was by recording 22 people over the span of 90 mins that EA was able to create enough animation, 8.75 million frames, for its latest 'HyperMotion technology. Machine learning then kicks in. "Previously, when a player moves towards the ball to complete the next move - such as such as a pass, shot or dribble - - we've selected an animation from our database," Eaves explains. "But it doesn't know the context. This data helps train the network and aids in blending animations."
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