Showing 4 open source projects for "three.js"

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    three.js

    three.js

    JavaScript 3D library

    three.js is a lightweight and easy-to-use JavaScript 3D library with its own default WebGL renderer. It works by creating a scene with a camera and a geometric cube, the latter of which is added to the scene. This will become the animated object within the scene. A WebGL renderer is created for the scene and the camera, with an added viewport to the document.body element.
    Downloads: 67 This Week
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  • 2
    react-three/flex

    react-three/flex

    Flexbox for react-three-fiber

    Placing content in THREE.js is hard. @react-three/flex brings the webs flexbox spec to react-three-fiber. It is based on Yoga, Facebook's open-source layout engine for react-native. Simply create layouts by wrapping your 3D objects in different <Box /> instances inside a <Flex /> container. This way they will be automatically placed in the 3D space following the flexbox specification just like in the DOM.
    Downloads: 0 This Week
    Last Update:
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  • 3
    jeelizFaceFilter

    jeelizFaceFilter

    Javascript/WebGL lightweight face tracking library

    This JavaScript library detects and tracks the face in real time from the camera video feed captured with WebRTC. Then it is possible to overlay 3D content for augmented reality applications. We provide various demonstrations using main WebGL 3D engines. We have included in this repository the release versions of the 3D engines to work with a determined version (they are in /libs/<name of the engine>/). This library is lightweight and it does not include any 3D engine or third-party library....
    Downloads: 0 This Week
    Last Update:
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  • 4
    TenorSpace.js

    TenorSpace.js

    Neural network 3D visualization framework

    TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information.
    Downloads: 0 This Week
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