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The next wave of dance instruction comes from Finland
The WAVE dance game is available in open-source code for anyone wanting to develop it further.
Markus LaattalaResearchers at Aalto University have unveiled a pioneering virtual reality (VR) technique that makes it easy to learn dance routines.
The new technique, WAVE, uses the concept of an audience wave, a familiar sight in sporting events, to guide a dancer. Where conventional VR methods rely on pre-rehearsal and simplification, WAVE allows the user to see in advance what kind of movement is coming by following the wave-like movement of virtual model dancers. Users can quickly join a dance routine without learning the choreography when they can anticipate the next move.
The researchers initially experimented with visualisation techniques similar to those used in dance games but soon moved on to the audience wave.
“No one had figured out how to guide a continuous, fluid movement like contemporary dance,”’ said Perttu Hämäläinen, professor at Aalto University. “In the choreography we implemented, making a wave is communication, a kind of micro-canon in which the model dancers follow the same choreography with a split-second delay.”
Wide range of applications
The WAVE technique proved efficient in a one-minute dance test involving 36 participants. It was compared to a traditional virtual version with only one dancer to follow. The results revealed that the technique is suitable, in particular, for slow-paced dance styles.
“The dancer can just jump in and start dancing without having to learn anything beforehand," Hämäläinen explained. "However, in faster movements, the visuals can get confusing, and further research and development is needed to adapt and test the approach with more dance styles."
There are several potential VR applications for the WAVE technique.
Aleksi PoutanenLooking beyond virtual dance games, the technique holds promise in diverse domains, including music videos, karaoke and tai chi. While there are limitations, such as mainstream VR devices not tracking leg movements, the researchers are actively working on enhancing the model.
The technique will be presented in May at the prestigious Human Factors in Computing Systems (CHI) conference in Hawaii.