AI-driven Video Frame Interpolation: Automating Animation and Videos

One of the handful of businesses gaining steam during COVID lockdown is online video streaming. From web series and Internet TV to online learning and marketing, video content has dominated the digital landscape with maximum user attention. However, high-quality and immersive visual experience that is imperative for business profitability is painstakingly difficult to produce, until we found artificial intelligence (AI). This article highlights one such AI-driven video frame interpolation technique using neural networks.

Simply put, video frame interpolation intelligently produces missing video frames between the original ones to enhance the video’s quality and resolution. To expand the scope of AI development services, Oodles AI explores how deep learning-powered video optimization can improve customer experience across business channels.

The Science Behind AI-driven Video Frame Interpolation
Deep learning offers several methodologies such as Adaptive Separable Convolution, Deep Voxel Flow, and others for synthesizing video frames. This article focuses on “Depth-aware Video Frame Interpolation”, or DAIN that deploys interpolation algorithms to detect the occlusion in videos using their depth information. The technique was first published by Wenbo Bao and a team of other Google researchers, who propose,

“A depth-aware flow projection layer that encourages sampling of closer objects than farther ones.”

The proposed DAIN model learns about the hierarchal features by collecting contextual information from neighboring pixels in each frame. For synthesizing the output frames, the tool wraps the input frames along with depth maps, and contextual features based on the optical flow and local interpolation kernels, as elaborated in the architecture below-

As apparent, the model comprises of five submodules, namely the flow estimation, depth estimation, context extraction, kernel estimation, and frame synthesis networks.

The model can efficiently enhance a video’s quality and temporal resolution by raising the rate of frames to as much as 60 per second.

Coupled with colorization techniques, the DAIN tool can sharpen and smooth the visuals and eliminate the blurriness in old video clips to provide an immersive visual experience, as compared below-

Similar to other machine learning solutions like image upscaling and voice cloning, this AI-driven video frame interpolation mechanism is finding many buyers across verticals to maximize user experience.

Let’s explore how businesses can provide optimum engagement and immersive visual experience to their customers using DAIN.

Potential Use Cases of AI-driven Video Frame Interpolation
AI-driven video frame interpolation has gained significant traction in the computer vision community to fuel highly innovative applications, such as-

Slow Motion Video Generation
Slow Motion or SloMO is an emerging Computer Vision technique aimed at interpolating video frames to create smooth and high-resolution video streams. The AI-driven video frame interpolation, DAIN churns the frame rate and resolution from the corresponding low-resolution and low frame rate video frames.

A deep learning practitioner, Noomkrad demonstrates the super-resolution capability of DAIN for the survival horror game, Resident Evil 2

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AI-driven Video Frame Interpolation: Automating Animation and Videos

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