Posted by , , Google. is essentially an image (or image channel) that contains information relating to the distance of the surfaces of objects in the scene from a given viewpoint (in this case, the camera itself) for every pixel in that image. Depth maps are a fundamental building block for a variety of computer graphics and computer vision applications, such as , and , the majority of photographs on the web are still missing associated depth maps. This, combined with users from the web community expressing a growing interest in having depth capabilities within JavaScript to enhance existing web apps such as to bring images to live, apply real time AR effects to a human face and body, or even reconstruct items for use in VR environments, helped shape the path for what you see today.
Today we are introducing the , which utilizes the predicted depth and enables a 3D parallax effect on the given portrait image. Try the live demo below, everyone can easily make their social media profile photo 3D as shown below.
At the core of the Portrait Depth API is a deep learning model, named ARPortraitDepth, that takes a single color portrait image as the input and produces a depth map. For the sake of computational efficiency, we adopt a light-weight U-Net architecture. As shown below, the encoder gradually downscales the image or feature map resolution by half, and the decoder increases the feature resolution to the same as the input. Deep learning features from the encoder are concatenated to the corresponding layers with the same spatial resolution in the decoders to bring high resolution signals for depth estimation. During training, we force the decoder to produce depth predictions with increasing resolutions at each layer, and add a loss for each of them with the ground truth. This empirically helps the decoder to predict accurate depth by gradually adding details. Abundant and diverse training data is critical for the machine learning model to achieve overall decent performance, e.g. accuracy and robustness. We synthetically render pairs of color and depth images with various camera configurations, e.g. focal length, camera pose, from 3D digital humans captured by with High Dynamic Range environment illumination maps to increase the realism and diversity of the color images, e.g. shadows on the face. We also collect real data using mobile phones equipped with a front facing depth sensor, e.g. To enhance the robustness against background variation, in practice, we run an off-the-shelf for more inspirations. For the 3D photo application, we created a high-performance rendering pipeline. It first generates a segmented mask using the TensorFlow.js existing , with vertices arranged in a regular grid and displaced by re-projecting corresponding depth values (see the figure below for generating the depth mesh). Finally, we apply texture projection to the depth mesh and rotate the camera around the z axis in a circle. Users can download the animations in GIF or WebM format. Thematisch verwandte Begriffe: Portrait, Depth, Turning, Single · 6 Treffer Videos werden geladen ... Beiträge werden geladen ... Videos werden geladen ... Beiträge werden geladen ... Videos werden geladen ... Beiträge werden geladen ... Videos werden geladen ... Beiträge werden geladen ... Videos werden geladen ...
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Single image depth estimation pipeline. , , , , , , . We would also like to acknowledge the for high quality synthetic data. Community-Analysen & Experten-Meinungen 0
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