8 NEWSFEED Updates & upgrades
Facebook has put forward a new unit for measuring time, called the ‘flick’. The flick is 1/705,600,000 of a second, just slightly longer than a nanosecond. The flick was invented to more clearly measure frame rates. It was originally proposed by Christopher Horvath while he was at Facebook in 2016. According to a description Facebook posted on Gizmodo, the flick “can in integer quantities exactly represent a single frame duration for 24Hz, 25Hz, 30Hz, 48Hz, 50Hz, 60Hz, 90Hz, 100Hz, 120Hz and also 1/1000 divisions of each, as well as a single sample duration for 8kHz, 16kHz, 22.05kHz, 24kHz, 32kHz, 44.1kHz, 48kHz, 88.2kHz, 96kHz, and 192kHz, as well as the NTSC frame durations for 24 * (1000/1001) Hz, 30 * (1000/1001) Hz, 60 * (1000/1001) Hz and 120 * (1000/1001) Hz.” FACEBOOK INVENTS A NEW UNIT OF TIME
It’s hoped the flick will also lead to simpler and more accurate measurement in graphics creation, visual eects, VR and other complex media creation. “When creating visual eects for film, television and other media,” said the posted description, “it is common to run simulations or other time-integrating processes which subdivide a single frame of time into a fixed, integer number of subdivisions. It is handy to be able to accumulate these subdivisions to create exact 1-frame and 1-second intervals, for a variety of reasons.” Facebook is also experimenting with full body tracking. The Facebook AI camera team has been working on tech that can accurately detect body poses and segment a person from their background. Potential commercial
uses of the technology include live, full-body avatars and gesture control. The Facebook human detection and segmentation model technology is designed to be small enough to sit within a smartphone app and is using the Mask R-CNN framework. In an announcement on the Facebook Research page, the company said: “Developing computer vision models for mobile devices is a challenging task. A mobile model has to be small, fast and accurate without large memory requirements. We will continue exploring new model architectures which will lead to more eicient models. We will also explore models that can better fit in mobile GPUs and DSPs, which has the potential to save both the battery and computational power.”
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