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
Code for "AutoMTL: A Programming Framework for Automated Multi-Task Learning"

AutoMTL: A Programming Framework for Automated Multi-Task Learning This is the website for our paper "AutoMTL: A Programming Framework for Automated M

Ivy Zhang 40 Dec 04, 2022
Dense Prediction Transformers

Vision Transformers for Dense Prediction This repository contains code and models for our paper: Vision Transformers for Dense Prediction René Ranftl,

Intel ISL (Intel Intelligent Systems Lab) 1.3k Dec 28, 2022
Bayesian Generative Adversarial Networks in Tensorflow

Bayesian Generative Adversarial Networks in Tensorflow This repository contains the Tensorflow implementation of the Bayesian GAN by Yunus Saatchi and

Andrew Gordon Wilson 1k Nov 29, 2022
Generate image analogies using neural matching and blending

neural image analogies This is basically an implementation of this "Image Analogies" paper, In our case, we use feature maps from VGG16. The patch mat

Adam Wentz 3.5k Jan 08, 2023
A very short and easy implementation of Quantile Regression DQN

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
Delta Conformity Sociopatterns Analysis - Delta Conformity Sociopatterns Analysis

Delta_Conformity_Sociopatterns_Analysis ∆-Conformity is a local homophily measur

2 Jan 09, 2022
PyTorch Code of "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics"

Memory In Memory Networks It is based on the paper Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spati

Yang Li 12 May 30, 2022
Custom IMDB Dataset is extracted between 2020-2021 and custom distilBERT model is trained for movie success probability prediction

IMDB Success Predictor Project involves Web Scraping custom IMDB data between 2020 and 2021 of 10000 movies and shows sorted by number of votes ,fine

Gautam Diwan 1 Jan 18, 2022
Official PyTorch implementation of CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds Introduction This is the official PyTorch implementation of o

Yijia Weng 96 Dec 07, 2022
Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time.

BBB Face Recognizer Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time. Instalati

Rafael Azevedo 232 Dec 24, 2022
Implementation of "Large Steps in Inverse Rendering of Geometry"

Large Steps in Inverse Rendering of Geometry ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia), December 2021. Baptiste Nicolet · Alec Jacob

RGL: Realistic Graphics Lab 274 Jan 06, 2023
A web application that provides real time temperature and humidity readings of a house.

About A web application which provides real time temperature and humidity readings of a house. If you're interested in the data collected so far click

Ben Thompson 3 Jan 28, 2022
Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

ARM-Net Dependencies Python 3.6 Pytorch 1.7 Results Train Data preprocessing cd data_scripts python extract_subimages_test.py python data_augmentation

Bohong Chen 55 Nov 24, 2022
Genetic Programming in Python, with a scikit-learn inspired API

Welcome to gplearn! gplearn implements Genetic Programming in Python, with a scikit-learn inspired and compatible API. While Genetic Programming (GP)

Trevor Stephens 1.3k Jan 03, 2023
Code and hyperparameters for the paper "Generative Adversarial Networks"

Generative Adversarial Networks This repository contains the code and hyperparameters for the paper: "Generative Adversarial Networks." Ian J. Goodfel

Ian Goodfellow 3.5k Jan 08, 2023
f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation

f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation [Paper] [PyTorch] [MXNet] [Video] This repository provides code for training

Visual Understanding Lab @ Samsung AI Center Moscow 516 Dec 21, 2022
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning

This is a release of our VIMPAC paper to illustrate the implementations. The pretrained checkpoints and scripts will be soon open-sourced in HuggingFace transformers.

Hao Tan 74 Dec 03, 2022
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

Deep Learning with TensorFlow 2 and Keras – Notebooks This project accompanies my Deep Learning with TensorFlow 2 and Keras trainings. It contains the

Aurélien Geron 1.9k Dec 15, 2022
A simple baseline for 3d human pose estimation in PyTorch.

3d_pose_baseline_pytorch A PyTorch implementation of a simple baseline for 3d human pose estimation. You can check the original Tensorflow implementat

weigq 312 Jan 06, 2023
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

SimGNN ⠀⠀⠀ A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity s

Benedek Rozemberczki 534 Dec 25, 2022