Oracle to Open GraphPipe Source Code

GraphPipe is a tool that simplifies the maintenance of machine learning models
17 August 2018   302

Oracle has opened the source code of the GraphPipe tool to simplify the maintenance of machine learning models. It supports projects based on the TensorFlow, MXNet, Caffe2 and PyTorch libraries. They are intended for use in IoT-devices, custom web-services and corporate AI-platforms.

The tool eliminates the need for developers to create custom APIs. Also, it eliminates confusion when using multiple frameworks and prevents memory copying during deserialization. The developers hope that GraphPipe will become a standard tool for deploying models.

GraphPipe is free and available on GitHub. It consists of open source tools designed to work with artificial intelligence. For example, the TensorFlow framework and the Open Neural Network Exchange (ONNX) project for creating portable neural networks are among them.

In September 2017, Microsoft introduced own tools for operating with machine learning. At the same time, the company released utilities for using Visual Studio Code when creating models based on the CNTK and Keras frameworks.

AI to be Used to Create 3D Motion Sculptures

The system developed by the MIT and Berkeley scientists is called MoSculp and is based on artificial inteligence
21 September 2018   119

MoSculp, the joint work of MIT scientists and the University of California at Berkeley, is built on the basis of a neural network. The development analyzes the video recording of a moving person and generates what the creators called "interactive visualization of form and time." According to the lead specialist of the project Xiuming Zhang, software will be useful for athletes for detailed analysis of movements.

At the first stage, the system scans the video frame-by-frame and determines the position of key points of the object's body, such as elbows, knees, ankles. For this, scientists decided to resort to the OpenPose library, developed by the Carnegie Mellon University. Based on the received data, the neural network compiles a 3D model of the person in each frame, and calculates the trajectory of the motion, obtaining a "motion sculpture".

At this stage, the image, according to the developers, suffers from a lack of textures and details, so the application integrates the "sculpture" in the original video. To avoid overlapping, MoSculp calculates a depth map for the original object and the 3D model.

MoSculp 3D Model
MoSculp 3D Model

The operator can adjust the image during the processing, select the "sculpture" material, color, lighting, and also what parts of the body will be tracked. The system is able to print the result using a 3D printer.

The team of researchers announced plans to further develop the MoSculp technology. Developers want to achieve from the processing system more than one object on the video, which is currently impossible. The creators of the technology believe that the program will be used to study group dynamics, social disorders and interpersonal interactions.

The principle of creating a 3D model based on human movements has been used before. For example, in August 2018, scientists at the same University of California at Berkeley demonstrated an algorithm that transfers the movements of one person to another.