Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad to your characters in Modo.

Overview

Applicator Kit for Modo

Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad with a TrueDepth camera to your characters in Modo. Apple ARKit Face Tracking enables your iPhone or iPad to track a performer’s head location as well as over 50 unique Blend Shape coefficients (Morph Targets in Modo), all at 60 frames per second. With Applicator Kit for Modo, you can take this data and apply it to your characters in Modo in 4 Easy Steps:

  1. Define your mapping file
  2. Record your face capture performance
  3. Transfer the data to your computer
  4. Apply the data to your character

Overview Videos:

Installation:

  1. Open Modo
  2. System > Open User Folder
  3. Copy the Applicator folder into the Kits folder
  4. Restart Modo

Key Features:

  • Item Hierarchy Target: apply the data to all mapped targets within a hierarchy of items in a scene
  • Actor and Action Target: apply the data to an Actor, and optionally as an Action (new or existing)
  • Mapping File: allows you to configure the target Morph Maps and Items to apply tracking data to
  • Multi-Target: allows you to apply a single Blend Shape tracking data to multiple Morph Maps
  • Independent Enable/Disable: gives you full control over which data points to apply to your scene
  • Multiplier: sometimes the capture is just too subtle (or too extreme) and not giving you the performance, you need. The multiplier allows you increase (or decrease) the value of the tracking data to your scene
  • Value Shift: like the multiplier, the value shift allows you to tweak the performance, but rather than multiplying the tracking data, it shifts the value up or down using a constant value (super handy for adjusting head rotation data)
  • Smoothing Algorithm: optionally apply a smoothing algorithm to the tracking data
  • FPS Conversion: automatically converts the 60fps recording data to scene’s fps. Support fps options: 60, 50, 48, 30, 29.97, 25 and 24.
  • Neutral Algorithm: by optionally providing a neutral facial capture (~5 seconds recording of the performer’s face in a neutral state), the algorithm adjusts the capture data to cater for the unique facial shape of the performer.
  • Start Frame: specify which frame to start the data application to
  • Skip Capture Frames: specify how many frames from the recording you’d like to skip

Supported Face Tracking Apps:

Note: Applicator Kit does not capture face tracking data, it only applies the data to your scenes in Modo. Please use Live Link Face (free courtesy of Unreal Engine) to capture the facial performance.

Owner
Andrew Buttigieg
Andrew Buttigieg
Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality".

personalized-breath Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality". Guideline To ex

Manh-Ha Bui 2 Nov 15, 2021
Safe Bayesian Optimization

SafeOpt - Safe Bayesian Optimization This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also p

Felix Berkenkamp 111 Dec 11, 2022
DeLighT: Very Deep and Light-Weight Transformers

DeLighT: Very Deep and Light-weight Transformers This repository contains the source code of our work on building efficient sequence models: DeFINE (I

Sachin Mehta 440 Dec 18, 2022
PyJokes - Joking around with Python library pyjokes

Hi, it's Muhaimin again 👋 This is something unorthodox but cool. Don't forget t

Muhaimin A. Salay Kanton 1 Feb 02, 2022
CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels Accurate pressure drop estimat

Alejandro Montanez 0 Jan 21, 2022
Source code for "Understanding Knowledge Integration in Language Models with Graph Convolutions"

Graph Convolution Simulator (GCS) Source code for "Understanding Knowledge Integration in Language Models with Graph Convolutions" Requirements: PyTor

yifan 10 Oct 18, 2022
Liver segmentation using MONAI and pytorch

Machine Learning use case in the field of Healthcare. In this project MONAI and pytorch frameworks are used for 3D Liver segmentation.

Abhishek Gajbhiye 2 May 30, 2022
A module that used for encrypt code which includes RSA and AES

软件加密模块 requirement: Crypto,pycryptodome,pyqt5 本地加密信息为随机字符串 使用说明 命令行参数 -h 帮助 -checkWorking 检查是否能正常工作,后接1确认指令 -checkEndDate 检查截至日期,后接1确认指令 -activateCode

2 Sep 27, 2022
Implementation of "Semi-supervised Domain Adaptive Structure Learning"

Semi-supervised Domain Adaptive Structure Learning - ASDA This repo contains the source code and dataset for our ASDA paper. Illustration of the propo

3 Dec 13, 2021
Parsing, analyzing, and comparing source code across many languages

Semantic semantic is a Haskell library and command line tool for parsing, analyzing, and comparing source code. In a hurry? Check out our documentatio

GitHub 8.6k Dec 28, 2022
A minimal implementation of face-detection models using flask, gunicorn, nginx, docker, and docker-compose

Face-Detection-flask-gunicorn-nginx-docker This is a simple implementation of dockerized face-detection restful-API implemented with flask, Nginx, and

Pooya-Mohammadi 30 Dec 17, 2022
Modelisation on galaxy evolution using PEGASE-HR

model_galaxy Modelisation on galaxy evolution using PEGASE-HR This is a labwork done in internship at IAP directed by Damien Le Borgne (https://github

Adrien Anthore 1 Jan 14, 2022
Calculates carbon footprint based on fuel mix and discharge profile at the utility selected. Can create graphs and tabular output for fuel mix based on input file of series of power drawn over a period of time.

carbon-footprint-calculator Conda distribution ~/anaconda3/bin/conda install anaconda-client conda-build ~/anaconda3/bin/conda config --set anaconda_u

Seattle university Renewable energy research 7 Sep 26, 2022
(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation

Inverse Q-Learning (IQ-Learn) Official code base for IQ-Learn: Inverse soft-Q Learning for Imitation, NeurIPS '21 Spotlight IQ-Learn is an easy-to-use

Divyansh Garg 102 Dec 20, 2022
CVPR 2020 oral paper: Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax.

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax ⚠️ Latest: Current repo is a complete version. But we delet

FishYuLi 341 Dec 23, 2022
Image-Stitching - Panorama composition using SIFT Features and a custom implementaion of RANSAC algorithm

About The Project Panorama composition using SIFT Features and a custom implementaion of RANSAC algorithm (Random Sample Consensus). Author: Andreas P

Andreas Panayiotou 3 Jan 03, 2023
tree-math: mathematical operations for JAX pytrees

tree-math: mathematical operations for JAX pytrees tree-math makes it easy to implement numerical algorithms that work on JAX pytrees, such as iterati

Google 137 Dec 28, 2022
ACL'2021: LM-BFF: Better Few-shot Fine-tuning of Language Models

LM-BFF (Better Few-shot Fine-tuning of Language Models) This is the implementation of the paper Making Pre-trained Language Models Better Few-shot Lea

Princeton Natural Language Processing 607 Jan 07, 2023
The code for two papers: Feedback Transformer and Expire-Span.

transformer-sequential This repo contains the code for two papers: Feedback Transformer Expire-Span The training code is structured for long sequentia

Facebook Research 125 Dec 25, 2022
CellRank's reproducibility repository.

CellRank's reproducibility repository We believe that reproducibility is key and have made it as simple as possible to reproduce our results. Please e

Theis Lab 8 Oct 08, 2022