3/28/2023 0 Comments Virtual pc builder![]() Now Virtual PC console will be seen as shown below:ġ3. Click on "Networking" and verify that the VirtualPC is sharing the correct network (This is important, because the virtual PC needs a DHCP server for downloading the OS image from the PB.ġ2. ![]() Select the path : Drive:/WINCE700/platform/VirtualPC/VM. Select "Add an existing virtual machine" and click "Next>".Ħ. ![]() Once compilation is successful, you are ready with VirtualPC OS image!ģ. Click on "New" to create a new virtual machine.ĥ. (It will be good if you select "Small Footprint Image" and add required shell and display options).įollow the below turtorial, if you don't know how to create a new OSDesign.Ĥ. (Select VirtualPC in a new OSDesign project). Make sure you have installed VirtualPC BSP (This comes as part of platform builder installation).Ģ. Follow the instructions to complete the installation.ġ. Install the VirtualPC by opening the setup file. Download "Microsoft VirtualPC Setup" from the link : Ģ. We will keep updating this project to provide ARM templates to easily deploy the backend on Azure as well as improving the comments and documentation.1.Go to GitHub where you will find the source code for both the front-end app and the server side as well as the user manual for the user app.From the speaker/content creator side, you will need 1 or 2 Azure Kinect sensors (plus applicable hardware/software requirements as listed).How can you use this project for your own events/videos? We also refine through an unsupervised GAN to improve the results even more. So what we input to this first model is the sensor information (both the IR and silhouette as well as the video without any processing) and the background without the speaker (captured from the front-end app by the user) - giving us a more precise foreground and alpha estimation as an output. The first step is done by a deep network that estimates the foreground and alpha from input comprised of the original image, the background photo, and an automatically computed soft segmentation of the person in frame.īy combining this approach with the Azure Kinect API, we can replace the automatically computed soft segmentation of the person in frame with the more precise silhouette captured with our sensor. The UW approach proposes 2 steps: – first, extract the background based in supervised learning and second, refine the output in an unsupervised way through a GAN. This is where the capabilities of Azure Kinect have helped. Yet, that still doesn’t solve for the lack of precision when it comes to recognizing the legs. To solve the bounding box issue, we split the image in two. This limitation not only means that legs are not properly processed by the model due to lack of training data, but also that the neural net output is a square bounding box – perfect for just upper-body images but not ideal for a full body. One of the key challenges we tried to solve in our implementation is that the dataset used for training (Adobe Composition-1k dataset) contained only upper body images - and we wanted to capture our presenters in long shots. This process leverages the approach by the University of Washington team we added improvements so we can utilize the depth recording from the Azure Kinect sensor to remove the background more precisely. The output of this process is the video with just the presenter (the rest being a virtual green screen). ![]() Once the user provides the video files, we need to create the virtual green screen so that we can edit in the software of our choice. This makes the process easier for the post-production team to ensure the best possible results in a remote collaboration scenario. ![]() We have also included in the front-end app an easy wizard to allow the user to record the background without them, in order to solve for the foreground and alpha value of the matting process. The app allows the presenter to review multiple recordings and keep the best ones before sending to the back-end process to generate the virtual green screen. It can record 3 video files from a single recording: up to two videos from Azure Kinect sensors and one separate file for the presenter’s slides with audio, to make post-production easier. The presenter uses the app to record themselves. This project consists of 2 different elements: ![]()
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