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
Rotation-Only Bundle Adjustment

ROBA: Rotation-Only Bundle Adjustment Paper, Video, Poster, Presentation, Supplementary Material In this repository, we provide the implementation of

Seong 51 Nov 29, 2022
Neural style transfer as a class in PyTorch

pt-styletransfer Neural style transfer as a class in PyTorch Based on: https://github.com/alexis-jacq/Pytorch-Tutorials Adds: StyleTransferNet as a cl

Tyler Kvochick 31 Jun 27, 2022
Pytorch Implementation for Dilated Continuous Random Field

DilatedCRF Pytorch implementation for fully-learnable DilatedCRF. If you find my work helpful, please consider our paper: @article{Mo2022dilatedcrf,

DunnoCoding_Plus 3 Nov 13, 2022
Search and filter videos based on objects that appear in them using convolutional neural networks

Thingscoop: Utility for searching and filtering videos based on their content Description Thingscoop is a command-line utility for analyzing videos se

Anastasis Germanidis 354 Dec 04, 2022
Avalanche RL: an End-to-End Library for Continual Reinforcement Learning

Avalanche RL: an End-to-End Library for Continual Reinforcement Learning Avalanche Website | Getting Started | Examples | Tutorial | API Doc | Paper |

ContinualAI 43 Dec 24, 2022
Compute descriptors for 3D point cloud registration using a multi scale sparse voxel architecture

MS-SVConv : 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning Compute features for 3D point cloud registration

42 Jul 25, 2022
Face Identity Disentanglement via Latent Space Mapping [SIGGRAPH ASIA 2020]

Face Identity Disentanglement via Latent Space Mapping Description Official Implementation of the paper Face Identity Disentanglement via Latent Space

150 Dec 07, 2022
A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor

Phase-SLAM A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor This open source is written by MATLAB Run Mode Open

Xi Zheng 14 Dec 19, 2022
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features

CleanRL (Clean Implementation of RL Algorithms) CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation

Costa Huang 1.8k Jan 01, 2023
Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

vid2vid Project | YouTube(short) | YouTube(full) | arXiv | Paper(full) Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic vid

NVIDIA Corporation 8.1k Jan 01, 2023
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

CQL-JAX This repository implements Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX (FLAX). Implementation is built on

Karush Suri 8 Nov 07, 2022
Bag of Tricks for Natural Policy Gradient Reinforcement Learning

Bag of Tricks for Natural Policy Gradient Reinforcement Learning [ArXiv] Setup Python 3.8.0 pip install -r req.txt Mujoco 200 license Main Files main.

Brennan Gebotys 1 Oct 10, 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations

HIVE: Evaluating the Human Interpretability of Visual Explanations Project Page | Paper This repo provides the code for HIVE, a human evaluation frame

Princeton Visual AI Lab 16 Dec 13, 2022
A simple pygame dino game which can also be trained and played by a NEAT KI

Dino Game AI Game The game itself was developed with the Pygame module pip install pygame You can also play it yourself by making the dino jump with t

Kilian Kier 7 Dec 05, 2022
Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021

MRefG Wanli Li and Tieyun Qian: "Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction", IJCNN 2021 1. Requirements To reproduc

万理 5 Jul 26, 2022
Imagededup - 😎 Finding duplicate images made easy

imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection.

idealo 4.3k Jan 07, 2023
OCRA (Object-Centric Recurrent Attention) source code

OCRA (Object-Centric Recurrent Attention) source code Hossein Adeli and Seoyoung Ahn Please cite this article if you find this repository useful: For

Hossein Adeli 2 Jun 18, 2022
Real-Time Multi-Contact Model Predictive Control via ADMM

Here, you can find the code for the paper 'Real-Time Multi-Contact Model Predictive Control via ADMM'. Code is currently being cleared up and optimize

17 Dec 28, 2022
PyTorch implementation of the Deep SLDA method from our CVPRW-2020 paper "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis"

Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis This is a PyTorch implementation of the Deep Streaming Linear Discriminant

Tyler Hayes 41 Dec 25, 2022
Rax is a Learning-to-Rank library written in JAX

πŸ¦– Rax: Composable Learning to Rank using JAX Rax is a Learning-to-Rank library written in JAX. Rax provides off-the-shelf implementations of ranking

Google 247 Dec 27, 2022