Author: Fritz

Synthesizing and editing video portraits—i.e., videos framed to show a person’s head and upper body—is an important problem in computer graphics, with applications in video editing and movie postproduction, visual effects, visual dubbing, virtual reality, and telepresence, among others.

The problem of synthesizing a photo-realistic video portrait of a target actor that mimics the actions of a source actor—and especially where the source and target actors can be different subjects—is still an open problem.

Continue reading Deep Video Portraits

There’s a saying that goes like this: A picture is worth a thousand words, while an animation is worth a thousand pictures (which in turn means that it is worth a million words 😉)!

This is especially true in mobile applications, where effective animations can communicate to users what an app (or certain features within an app) is supposed to do. This is always a tough task, but tougher when we only rely on text or static images.

Continue reading Using Lottie on Android to display dynamic animations in your apps

In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video. This could be in between video frames, known as interpolation, or after them, known as extrapolation.

The better part of this guide will cover interpolation. Interpolation is useful in software editing tools as well as in generating video animations. It can also be used to generate clear video frames in sections where a video is blurred.

Continue reading Research Guide for Video Frame Interpolation with Deep Learning

Apple uses natural language processing techniques in many ways on iOS. Thanks to NLP, iOS can auto-fix typos, and Siri can understand what we’re saying. At WWDC 2018, Apple brought these capabilities to developers via a tool called Create ML. This tool has enabled developers to easily create text classification models (among numerous other kinds of models).

Continue reading Text Classification on iOS Using Create ML

In its most general sense, the word dispatch describes the act of sending something somewhere for a particular purpose. In computer science, this term is used to indicate the same concept in different contexts, like to dispatch a call to a function, dispatch an event to a listener, dispatch an interrupt to a handler, or dispatch a process to the CPU.

Continue reading Understanding method dispatch in Swift

Optical character recognition (OCR) is a method that helps machines recognize texts. Traditional OCR uses patterns and correlation to differentiate words from other elements. However, these techniques don’t tend to produce results with high accuracy for complex text or in-motion streams. For these tasks, you’re better served using deep learning models. In that spirit, in this article we’ll explore three deep learning models for OCR.

Continue reading Optical Character Recognition Using Deep Learning Techniques

In recent years, research related to vision-based 3D image processing has become increasingly active, given its many applications in virtual reality (VR) and augmented reality (AR). Despite years of studies, however, there are still images that machines struggle to understand—one of those is images of human hands.

Hand image understanding targets the problem of recovering the spatial configuration of hands from natural RGB or/and depth images. This task has many applications, such as human-machine interaction and virtual/augmented reality, among others.

Continue reading HAMR — 3D Hand Shape and Pose Estimation from a Single RGB Image

In this tutorial, we’ll create a car that controlled using a USB camera and a Raspberry Pi. We’ll use a wood plank as the car body, which will hold all the car’s components. The components are:

After fixing the component using cable ties, we’ll connect the Raspberry Pito the motors using its general purpose input output (GPIO) pins. Similar to a traffic sign, the motors will run when the dominant color in the captured images is green and stop when it’s red.

Continue reading Building a Vision-Controlled Car Using Raspberry Pi—From Scratch

In this piece, we’ll take a closer look at some of the top machine learning articles in 2019, as ranked by MyBridge.

According to the article:

Pluribus was tested against professional poker players. This included two winners of the World Series of Poker Event. Pluribus uses self-play to teach itself how to win. This happens without any examples or guidance. The self-play version used in Pluribus is an improved version of the iterative Monte Carlo CFR (MCCFR) algorithm.

Continue reading 2019’s Top Machine Learning Articles