Microsoft Corporation (MSFT)’s ‘Hyperlapse’ Project Aims to Improve First-Person Videos


Microsoft Corporation (NASDAQ:MSFT)’s researchers Johannes Kopf, Richard Szeliski and Michael Cohen worked on the ‘Hyperlapse’ project, which intends to improve the quality of the footage from action cameras. The majority of people use action cameras from companies like GoPro Inc (NASDAQ:GPRO) to record their adventure, from the first person viewpoint. Researchers have published a technical paper about this technology.


Microsoft Corporation (NASDAQ:MSFT) researchers, who made the ‘Hyperlapse’ technology feel that the videos recorded by action cameras are too lengthy and boring to watch. They said that those videos can be made interesting by speeding them up with high speedup rates. But the resulting video amplifies shake in the first person video and make it difficult to follow.

They pointed out that the video stabilization techniques available to date, might not be able to improve the quality of time lapsed video. To solve this problem Kopf, Szeliski and Cohen created the ‘Hyperlapse’ algorithm, which produces smooth time lapse video without any violent camera shake. The difference was pretty obvious from the sample videos that they posted on the project’s page.

The ‘Hyperlapse’ algorithm follows a three step process to generate a smooth time lapsed video. The algorithm reconstructs the camera holders path and translates that into a smoothed-out path, while ensuring that the virtual camera looks in directions that can be rendered well from the input. 

Then the algorithm computes the geometric proxies for each frame which helps in rendering the frames from the novel viewpoints on the optimized path. Finally the algorithm generates a smoothed time lapse video by rendering, stitching and blending the source frames modified and generated in the first two steps.

Microsoft Corporation (NASDAQ:MSFT)’s researchers are targeting to make this ‘Hyperlapse’ algorithm available as a Windows app. They also intend to reduce the processing power requirement for this algorithm to make it workable across the platforms.

Watch the presentation of ‘Hyperlapse’ below:

Disclosure: none

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