Chandraker and his colleagues used the technique to process insect flight-motion videos provided by Assistant Research Professor Adrian Smith of North Carolina State University. FLAVR can also enhance the visual quality of broadcast, such as visualizing a fast projectile in shooting or archery, or split-second motions in motor racing.Īnother application of FLAVR has already been demonstrated in the area of animal research. In sports competition and broadcasting, for example, such super slow-motion can impact crucial decision-making around ambiguous events that happen "in-between frames," such as a cricket batsman reaching inside the crease while completing a run. Potential applications for FLAVR include sports analytics (replays, video assisted referrals, player analytics, etc.), gaming and animation (generating high frame-per-second graphics at a cheaper cost), or aesthetically improving videos (such as adding slo-mo filters to videos captured from mobile phones in real-time). Insects and birds in flight, motor racing and more “Most importantly FLAVR Is 14% better compared to architectures which run at the same speed and six times faster compared to methods that deliver the same accuracy, leading to the best speed vs accuracy trade off,” he said. FLAVR can also be used to apply slo-mo filters to videos captured in real time.Ĭhandraker said the team “consistently demonstrates superior qualitative and quantitative results compared with prior methods on popular benchmarks,” such as Vimeo-90K, Adobe-240FPS, and GoPro. accuracy trade-off, even while requiring no additional visual data (such as optical flow rate or depth maps). The results demonstrate the best speed vs. Their published results were selected as the Best Paper Finalist at the 2023 Winter Conference on Applications of Computer Vision and show a sixfold increase in speed for multi-frame interpolation as compared to current state-of-the-art methods. “Our work breaks new ground in video frame interpolation, wherein we do away with most of hand-designed, computation heavy modules like flow-warping and use a complete end-to-end trainable and deployable architecture for this purpose - as a result, we achieve huge improvements in running time, output quality as well as ease of deploying on hardware,” said Kalluri.
0 Comments
Leave a Reply. |