Kalidokit is a blendshape and kinematics solver for Mediapipe/Tensorflow.js face, eyes, pose, and hand tracking models

Overview

KalidoKit - Face, Pose, and Hand Tracking Kinematics

Kalidokit Template

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

ko-fi

Install

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/[email protected]/dist/kalidokit.umd.js"></script>

Methods

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
Face.solve(facelandmarkArray);
Pose.solve(poseWorld3DArray, poseLandmarkArray);
Hand.solve(handLandmarkArray, "Right");

Additional Utils

// Stabilizes left/right blink delays + wink by providing blenshapes and head rotation
Kalidokit.Face.stabilizeBlink(
    { 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
Kalidokit.Vector();

Remixable VRM Template with KalidoKit

Quick-start your Vtuber app with this simple remixable example on Glitch. 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!

Remix on Glitch

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.

{ await holistic.send({image: HTMLVideoElement}); }, width: 640, height: 480 }); camera.start(); ">
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/[email protected]/${file}`;
}});

holistic.onResults(results=>{
    // 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
});
camera.start();

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.

Kalidokit.Pose.solve(poselm3D,poselm,{
    runtime:'tfjs', // default is 'mediapipe'
    video: HTMLVideoElement,// specify an html video or manually set image size
    imageSize:{
        width: 640,
        height: 480,
    };
})

Kalidokit.Face.solve(facelm,{
    runtime:'mediapipe', // default is 'tfjs'
    video: HTMLVideoElement,// specify an html video or manually set image size
    imageSize:{
        width: 640,
        height: 480,
    };
})

Outputs

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, we would love to hear about it! Feel free to also use our Twitter hashtag, #kalidokit.

Kalidoface virtual webcam Kalidoface Pose Demo

Open to Contributions

The current library is a work in progress and contributions to improve it are very welcome. Our goal is to make character face and pose animation even more accessible to creatives regardless of skill level!

Owner
Rich
Making Vtuber apps with Mediapipe and Tensorflow.js
Rich
An image processing project uses Viola-jones technique to detect faces and then use SIFT algorithm for recognition.

Attendance_System An image processing project uses Viola-jones technique to detect faces and then use LPB algorithm for recognition. Face Detection Us

8 Jan 11, 2022
Submanifold sparse convolutional networks

Submanifold Sparse Convolutional Networks This is the PyTorch library for training Submanifold Sparse Convolutional Networks. Spatial sparsity This li

Facebook Research 1.8k Jan 06, 2023
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Trieu 6.1k Jan 04, 2023
A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

24 Dec 13, 2022
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

ACE Please find the preliminary version published at BMVC 2020 in the folder BMVC_version, and its extended journal version in Journal_version. Datase

28 Dec 25, 2022
gACSON software for visualization, processing and analysis of three-dimensional electron microscopy images

gACSON gACSON software is to visualize, segment, and analyze the morphology of neurons in three-dimensional electron microscopy images. If you use any

Andrea Behanova 2 May 31, 2022
Bringing sanity to world of messed-up data

Sanitize sanitize is a Python module for making sure various things (e.g. HTML) are safe to use. It was originally written by Mark Pilgrim and is dist

Alireza Savand 63 Oct 26, 2021
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

AnimeGAN A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. Randomly Generated Images The images are

Jie Lei 雷杰 1.2k Jan 03, 2023
Tacotron 2 - PyTorch implementation with faster-than-realtime inference

Tacotron 2 (without wavenet) PyTorch implementation of Natural TTS Synthesis By Conditioning Wavenet On Mel Spectrogram Predictions. This implementati

NVIDIA Corporation 4.1k Jan 03, 2023
Demonstrational Session git repo for H SAF User Workshop (28/1)

5th H SAF User Workshop The 5th H SAF User Workshop supported by EUMeTrain will be held in online in January 24-28 2022. This repository contains inst

H SAF 4 Aug 04, 2022
Official Implementation and Dataset of "PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency", CVPR 2021

Portrait Photo Retouching with PPR10K Paper | Supplementary Material PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask an

184 Dec 11, 2022
Bayesian regularization for functional graphical models.

BayesFGM Paper: Jiajing Niu, Andrew Brown. Bayesian regularization for functional graphical models. Requirements R version 3.6.3 and up Python 3.6 and

0 Oct 07, 2021
Project for tracking occupancy in Tel-Aviv parking lots.

Ahuzat Dibuk - Tracking occupancy in Tel-Aviv parking lots main.py This module was set-up to be executed on Google Cloud Platform. I run it every 15 m

Geva Kipper 35 Nov 22, 2022
Python periodic table module

elemenpy Hello! elements.py is a small Python periodic table module that is used for calling certain information about an element. Installation Instal

Eric Cheng 2 Dec 27, 2021
GLM (General Language Model)

GLM GLM is a General Language Model pretrained with an autoregressive blank-filling objective and can be finetuned on various natural language underst

THUDM 421 Jan 04, 2023
PaSST: Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation (NeurIPS2021 Benchmark and Dataset Track)

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Kingdrone 174 Dec 22, 2022
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution (MICCAI 2021)

T2Net Task Transformer Network for Joint MRI Reconstruction and Super-Resolution (MICCAI 2021) [Paper][Code] Dependencies numpy==1.18.5 scikit_image==

64 Nov 23, 2022
Neural models of common sense. 🤖

Unicorn on Rainbow Neural models of common sense. This repository is for the paper: Unicorn on Rainbow: A Universal Commonsense Reasoning Model on a N

AI2 60 Jan 05, 2023
Bagua is a flexible and performant distributed training algorithm development framework.

Bagua is a flexible and performant distributed training algorithm development framework.

786 Dec 17, 2022