Dance Me If You Can — Motion Transference using AI
This week’s Paper of the Week can be found here: Everybody Dance Now
I don’t know about you, but I’ve never won a dance-off. I wouldn’t consider myself a particularly bad dancer, but the fact of the matter is that there is almost always someone who shows up on the dance floor and starts busting out moves that most of us have only ever seen in choreographed music videos. Now, unlike many of my previous Papers of the Week, this week we are not talking about life or death medical scenarios. As much as we all hate to lose, by the next morning, we return to our normal lives, which presumably do not resemble Footloose.
Instead, we might assuage our dancing deficiencies by watching YouTube choreography or music videos with interesting dance scenes. We might even look into online dance competitions, but only as a spectator, never as a competitor. Until recently, I would wager that most people had come to terms with the current state of their dance skills and were not in a hurry to find a workaround to winning online dance competitions.
Fortunately for your future online dance competitions, researchers at the University of California, Berkeley, have your back. Using artificial intelligence, they’ve developed a way to transfer the motion of a dancer in a music video directly to you — well, a video of you.
Originally published at www.jordanharrod.com.
