Google proposed DynIBaR natural dynamic image rendering technology
Recently, Google unveiled a technology called “Neural Dynamic Image-Based Rendering” or DynIBaR. This avant-garde technique allows the automatic generation of intricate and dynamic scenes from a singular video. Moreover, it can adapt to various cinematic effects such as bullet time, image stabilization, slow-motion playback, and bokeh displays.
Historically, analogous image-rendering reconstruction methods demanded the storage of complex scene data within a singular structure. This data then underwent processing through deep perceptual neural networks. However, as the video duration and intricacy surged, more computational resources were essential, impacting computational efficiency. Additionally, the eventual outcomes were often suboptimal.
Contrastingly, DynIBaR primarily captures motion data and employs artificial intelligence to automatically generate images from the desired user perspectives. There’s no necessity to produce entirely new images from scratch, as it capitalizes on pre-existing pixel information to produce new frames. During its processing, DynIBaR fine-tunes to ensure optimal visual quality, maintaining scene data consistency across different timestamps. Furthermore, by separately rendering dynamic and static images, the overall image accuracy is amplified.
Within this technological paradigm, Google asserts its applicability in handheld camera stabilization, promising a significant reduction in image jitter and blurriness. Additionally, it can craft smoother 3D visual effects, quintuple slow-motion videos, and even manifest diverse bokeh effects, along with the aforementioned bullet time visuals.
Google envisages DynIBaR’s integration into mobile devices, enabling users to capture crisper videos and craft myriad cinematic effects using their smartphones.