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Disney researchers find a way to improve renderings of fabric

It’s the clothes that make the woman or man, and that principle holds true whether you’re an animated character or a flesh and blood human being. The most beloved of Disney characters are known as much for their outfits as they are for their plotlines, and now, the geniuses at the animation studio have found a more efficient way to render realistic fabrics, so it will take less time to make Elsa’s epic train can look even better.

Despite all the advances made in the animation world, finding ways to create digital fabrics that move, react to gravity, and otherwise look and behave like the real thing is still a difficult feat. But now, Disney Research has released a new paper about how they applied “the algebraic multigrid method known as smoothed aggregation to cloth simulation.” A joint effort between Walt Disney Animation Studios and the University of Colorado at Boulder, the team claims that it has managed to make more realistic-looking cloth six to eight times faster than currently possible.

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Problems with cloth animation are rooted in the inherent physics of fabric. As Tamstorf explained in a release, “When a piece of material is stretched, for instance, it not only gets longer in one direction, but also compresses, or gets narrower, in the perpendicular direction.” Whereas previously, animators often had to choose between quality of appearance and time spent on renderings, now, the trade-off may not be so extreme. The goal, explain authors Rasmus Tamstorf, Toby Jones, and Stephen McCormick, is ultimately to achieve the “acceleration of high-end cloth simulation, with applications ranging from feature film production to virtual try-on of garments in e-commerce.”

And although Tamstorf admits, “The idea is not new; other people have tried to use geometric multigrid methods for cloth simulation,” their latest attempt at a solution seems to be the most effective one to date.

So get ready for some seriously amazing Disney outfits. I know you didn’t think they could get any better, but we just may be surprised.