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3D Animation and Computer Graphics has become an almost essential skill today, especially if you work in Creative Industry or Immersive Technologies like AR / VR. You’ll often hear terms like WebGL, Particle Effect, Three.JS, Shaders, .fbx or .gltf file formats etc..

Learning 3D Animation or Motion Graphics can be very daunting at first, especially when you see a clumsy user-interface loaded with tons or menu and toolbar options.. Here I present a series of cool demos to simplify 3D Animation basics for beginners and learners, using a free but highly powerful 3D modeling, animation and rendering tool called Blender.

The first demo utilizes some key features of Blender, to create an array of 3D mesh objects (spheres or cubicles), applies random color material, and then wave modifier on underlying surface / plane to produce wave-like ripples or bouncing effect. …

Demo: Sports Activity Recognition using Transfer Learning

What is Transfer Learning?

While pre-trained models are great for recognizing cats and dogs, or cars and bicycles, very often, we want Machine Learning to do something more elaborate. Transfer Learning method utilizes intermediate layers (or in simple ML terms “features”) from a pre-trained model which is trained on a very large dataset. As a result, it achieves decent accuracy even though the new training set used for customization is relatively much smaller in size.

In this tutorial, I demonstrate how to build a knn-classifier for sports activity recognition task, using Transfer Learning from a pre-trained model Mobile-Net in TensorFlow.JS. …

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In this article, I show you how to perform image recognition & video understanding tasks in just 2 lines of code, using TensorFlow.JS

To detect objects :

model = cocoSsd.load();

objects = model.detect(frame);

Model output tested on Olympics horse-riding game..

For video tagging :

model = mobilenet.load();

tags = model.classify(frame);

Model output tested on wild-life documentary films..

To capture Facial Expressions :

model = facemesh.load();

faces = model.estimateFaces(frame);

Model output tested on live video stream to detect facial expressions..

For Hand-Tracking :

model = handpose.load();

result = model.estimateHands(frame);

Model tested on live camera stream to generate music using hand gestures.. …



Creative Media Technology || Website: pamruta.com

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