In other CVPR news, earlier today Microsoft CEO Satya Nadella reiterated the need for facial recognition regulation in prerecorded remarks, following the company’s commitment last week to not sell the technology until federal regulation is introduced. To curate video data sets, a convolutional neural network removes any frames that include hands, paintbrushes, and shadows. The frame interpolation method predicts the next sequence in generated time-lapse videos, as well as various AI style transfer techniques.

“To the best of our knowledge, this is the first work that models and synthesizes distributions of videos of the past, given a single final frame.”

“We show that human evaluators almost always prefer our method to an existing video synthesis baseline and often find our results indistinguishable from time-lapses produced by real artists,” the paper reads. Roughly 150 human evaluators were hired from Amazon’s Mechanical Turk as part of the experiment to compare video generated by MIT’s model with visual deprojection, a method for recovering missing frames from videos introduced at the Conference on Computer Vision and Pattern Recognition (CVPR) in 2019. Both data sets focus on landscape and still life paintings. The data set contains 117 digital painting time-lapse videos, averaging four minutes long, and 116 time-lapse watercolor painting videos, averaging 20 minutes each. The authors characterize their work as distinct from other forms of precognitive AI that predict future frames in a video because others tend to focus on physical processes like a flower blooming or human movement and make predictions over relatively short time frames. MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.

Ai devs time lapse tool