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    1.1.4 • Public • Published

    Kalidokit Template

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    Face, Pose, and Hand Tracking Calculator

    Kalidokit is a blendshape and kinematics solver for Mediapipe/Tensorflow.js face, eyes, pose, and hand tracking models, compatible with Facemesh, Blazepose, Handpose, and Holistic. It takes predicted 3D landmarks and calculates simple euler rotations and blendshape face values.

    As the core to Vtuber web apps, Kalidoface and Kalidoface 3D, KalidoKit is designed specifically for rigging 3D VRM models and Live2D avatars!

    Kalidokit Template Kalidokit Template



    Via NPM

    npm install kalidokit
    import * as Kalidokit from "kalidokit";
    // or only import the class you need
    import { Face, Pose, Hand } from "kalidokit";

    Via CDN

    <script src="https://cdn.jsdelivr.net/npm/kalidokit@1.1/dist/kalidokit.umd.js"></script>


    Kalidokit is composed of 3 classes for Face, Pose, and Hand calculations. They accept landmark outputs from models like Facemesh, Blazepose, Handpose, and Holistic.

    // Accepts an array(468 or 478 with iris tracking) of vectors
    Kalidokit.Face.solve(facelandmarkArray, {
        runtime: "tfjs", // `mediapipe` or `tfjs`
        video: HTMLVideoElement,
        imageSize: { height: 0, width: 0 },
        smoothBlink: false, // smooth left and right eye blink delays
        blinkSettings: [0.25, 0.75], // adjust upper and lower bound blink sensitivity
    // Accepts arrays(33) of Pose keypoints and 3D Pose keypoints
    Kalidokit.Pose.solve(poseWorld3DArray, poseLandmarkArray, {
        runtime: "tfjs", // `mediapipe` or `tfjs`
        video: HTMLVideoElement,
        imageSize: { height: 0, width: 0 },
        enableLegs: true,
    // Accepts array(21) of hand landmark vectors; specify 'Right' or 'Left' side
    Kalidokit.Hand.solve(handLandmarkArray, "Right");
    // Using exported classes directly
    Pose.solve(poseWorld3DArray, poseLandmarkArray);
    Hand.solve(handLandmarkArray, "Right");

    Additional Utils

    // Stabilizes left/right blink delays + wink by providing blenshapes and head rotation
        { r: 0, l: 1 }, // left and right eye blendshape values
        headRotationY, // head rotation in radians
            noWink = false, // disables winking
            maxRot = 0.5 // max head rotation in radians before interpolating obscured eyes
    // The internal vector math class

    VRM and Live2D Sample Code

    Quick-start your Vtuber app with these simple remixable examples.

    VRM Glitch Template

    Face, full-body, and hand tracking in under 350 lines of javascript. This demo uses Mediapipe Holistic for body tracking, Three.js + Three-VRM for rendering models, and KalidoKit for the kinematic calculations. This demo uses a minimal amount of easing to smooth animations, but feel free to make it your own! VRM model from Vroid Hub.

    Remix on Glitch

    Live2D Glitch Template

    This demo uses Mediapipe FaceMesh for face tracking, Pixi.js + pixi-live2d-display for rendering models, and KalidoKit for the kinematic calculations. This demo uses an official sample Live2D model, Hiyori.

    Remix on Glitch

    Local VRM and Live2D Examples

    Interested in contributing? If you clone this project, the docs folder includes both VRM and Live2D KalidoKit templates. Run the test script to build the library and start up the dev server. The VRM demo will be hosted on localhost:3000 and Live2D on localhost:3000/live2d/.

    npm run test

    Basic Usage

    Kalidokit Template

    The implementation may vary depending on what pose and face detection model you choose to use, but the principle is still the same. This example uses Mediapipe Holistic which concisely combines them together.

    import * as Kalidokit from 'kalidokit'
    import '@mediapipe/holistic/holistic';
    import '@mediapipe/camera_utils/camera_utils';
    let holistic = new Holistic({locateFile: (file) => {
        return `https://cdn.jsdelivr.net/npm/@mediapipe/holistic@0.4.1633559476/${file}`;
        // do something with prediction results
        // landmark names may change depending on TFJS/Mediapipe model version
        let facelm = results.faceLandmarks;
        let poselm = results.poseLandmarks;
        let poselm3D = results.ea;
        let rightHandlm = results.rightHandLandmarks;
        let leftHandlm = results.leftHandLandmarks;
        let faceRig = Kalidokit.Face.solve(facelm,{runtime:'mediapipe',video:HTMLVideoElement})
        let poseRig = Kalidokit.Pose.solve(poselm3d,poselm,{runtime:'mediapipe',video:HTMLVideoElement})
        let rightHandRig = Kalidokit.Hand.solve(rightHandlm,"Right")
        let leftHandRig = Kalidokit.Hand.solve(leftHandlm,"Left")
    // use Mediapipe's webcam utils to send video to holistic every frame
    const camera = new Camera(HTMLVideoElement, {
      onFrame: async () => {
        await holistic.send({image: HTMLVideoElement});
      width: 640,
      height: 480

    Slight differences with Mediapipe and Tensorflow.js

    Due to slight differences in the results from Mediapipe and Tensorflow.js, it is recommended to specify which runtime version you are using as well as the video input/image size as a reference.

        runtime:'tfjs', // default is 'mediapipe'
        video: HTMLVideoElement,// specify an html video or manually set image size
            width: 640,
            height: 480,
        runtime:'mediapipe', // default is 'tfjs'
        video: HTMLVideoElement,// specify an html video or manually set image size
            width: 640,
            height: 480,


    Below are the expected results from KalidoKit solvers.

    // Kalidokit.Face.solve()
    // Head rotations in radians
    // Degrees and normalized rotations also available
        eye: {l: 1,r: 1},
        mouth: {
            x: 0,
            y: 0,
            shape: {A:0, E:0, I:0, O:0, U:0}
        head: {
            x: 0,
            y: 0,
            z: 0,
            width: 0.3,
            height: 0.6,
            position: {x: 0.5, y: 0.5, z: 0}
        brow: 0,
        pupil: {x: 0, y: 0}
    // Kalidokit.Pose.solve()
    // Joint rotations in radians, leg calculators are a WIP
        RightUpperArm: {x: 0, y: 0, z: -1.25},
        LeftUpperArm: {x: 0, y: 0, z: 1.25},
        RightLowerArm: {x: 0, y: 0, z: 0},
        LeftLowerArm: {x: 0, y: 0, z: 0},
        LeftUpperLeg: {x: 0, y: 0, z: 0},
        RightUpperLeg: {x: 0, y: 0, z: 0},
        RightLowerLeg: {x: 0, y: 0, z: 0},
        LeftLowerLeg: {x: 0, y: 0, z: 0},
        LeftHand: {x: 0, y: 0, z: 0},
        RightHand: {x: 0, y: 0, z: 0},
        Spine: {x: 0, y: 0, z: 0},
        Hips: {
            worldPosition: {x: 0, y: 0, z: 0},
            position: {x: 0, y: 0, z: 0},
            rotation: {x: 0, y: 0, z: 0},
    // Kalidokit.Hand.solve()
    // Joint rotations in radians
    // only wrist and thumb have 3 degrees of freedom
    // all other finger joints move in the Z axis only
        RightWrist: {x: -0.13, y: -0.07, z: -1.04},
        RightRingProximal: {x: 0, y: 0, z: -0.13},
        RightRingIntermediate: {x: 0, y: 0, z: -0.4},
        RightRingDistal: {x: 0, y: 0, z: -0.04},
        RightIndexProximal: {x: 0, y: 0, z: -0.24},
        RightIndexIntermediate: {x: 0, y: 0, z: -0.25},
        RightIndexDistal: {x: 0, y: 0, z: -0.06},
        RightMiddleProximal: {x: 0, y: 0, z: -0.09},
        RightMiddleIntermediate: {x: 0, y: 0, z: -0.44},
        RightMiddleDistal: {x: 0, y: 0, z: -0.06},
        RightThumbProximal: {x: -0.23, y: -0.33, z: -0.12},
        RightThumbIntermediate: {x: -0.2, y: -0.19, z: -0.01},
        RightThumbDistal: {x: -0.2, y: 0.002, z: 0.15},
        RightLittleProximal: {x: 0, y: 0, z: -0.09},
        RightLittleIntermediate: {x: 0, y: 0, z: -0.22},
        RightLittleDistal: {x: 0, y: 0, z: -0.1}

    Community Showcase

    If you'd like to share a creative use of KalidoKit, please reach out or send a pull request! Feel free to also use our Twitter hashtag, #kalidokit.

    Kalidoface virtual webcam Kalidoface Pose Demo


    The current library is a work in progress and contributions to improve it are very welcome! Some plans include: better facial blendshapes, full leg tracking, and improved efficiency.


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