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
The implementation of the CVPR2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes"

STAR-FC This code is the implementation for the CVPR 2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes" 🌟 🌟 . 🎓 Re

Shuai Shen 87 Dec 28, 2022
Malmo Collaborative AI Challenge - Team Pig Catcher

The Malmo Collaborative AI Challenge - Team Pig Catcher Approach The challenge involves 2 agents who can either cooperate or defect. The optimal polic

Kai Arulkumaran 66 Jun 29, 2022
An open source implementation of CLIP.

OpenCLIP Welcome to an open source implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training). The goal of this repository is to enable

2.7k Dec 31, 2022
SW components and demos for visual kinship recognition. An emphasis is put on the FIW dataset-- data loaders, benchmarks, results in summary.

FIW Data Development Kit Table of Contents Introduction Families In the Wild Database Publications Organization To Do License Getting Involved Introdu

Joseph P. Robinson 12 Jun 04, 2022
My implementation of Fully Convolutional Neural Networks in Keras

Keras-FCN This repository contains my implementation of Fully Convolutional Networks in Keras (Tensorflow backend). Currently, semantic segmentation c

The Duy Nguyen 15 Jan 13, 2020
ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS.

ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS. It currently supports four examples for you to quickly experience the power of ONNX Runti

Microsoft 58 Dec 18, 2022
[arXiv'22] Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation

Panoptic NeRF Project Page | Paper | Dataset Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation Xiao Fu*, Shangzhan zhang*,

Xiao Fu 111 Dec 16, 2022
This is Official implementation for "Pose-guided Feature Disentangling for Occluded Person Re-Identification Based on Transformer" in AAAI2022

PFD:Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer This repo is the official implementation of "Pose-gui

Tao Wang 93 Dec 18, 2022
[ICCV'21] UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction Project Page | Paper | Supplementary | Video This reposit

331 Dec 28, 2022
交互式标注软件,暂定名 iann

iann 交互式标注软件,暂定名iann。 安装 按照官网介绍安装paddle。 安装其他依赖 pip install -r requirements.txt 运行 git clone https://github.com/PaddleCV-SIG/iann/ cd iann python iann

294 Dec 30, 2022
Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch

NÜWA - Pytorch (wip) Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch. This repository will be popul

Phil Wang 463 Dec 28, 2022
Research on controller area network Intrusion Detection Systems

Group members information Member 1: Lixue Liang Member 2: Yuet Lee Chan Member 3: Xinruo Zhang Member 4: Yifei Han User Manual Generate Attack Packets

Roche 4 Aug 30, 2022
This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

SBEVNet: End-to-End Deep Stereo Layout Estimation This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by D

Divam Gupta 19 Dec 17, 2022
Customizable RecSys Simulator for OpenAI Gym

gym-recsys: Customizable RecSys Simulator for OpenAI Gym Installation | How to use | Examples | Citation This package describes an OpenAI Gym interfac

Xingdong Zuo 14 Dec 08, 2022
A general and strong 3D object detection codebase that supports more methods, datasets and tools (debugging, recording and analysis).

ALLINONE-Det ALLINONE-Det is a general and strong 3D object detection codebase built on OpenPCDet, which supports more methods, datasets and tools (de

Michael.CV 5 Nov 03, 2022
This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

SeerNet This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is

3 May 01, 2022
Baseline inference Algorithm for the STOIC2021 challenge.

STOIC2021 Baseline Algorithm This codebase contains an example submission for the STOIC2021 COVID-19 AI Challenge. As a baseline algorithm, it impleme

Luuk Boulogne 10 Aug 08, 2022
Consecutive-Subsequence - Simple software to calculate susequence with highest sum

Simple software to calculate susequence with highest sum This repository contain

Gbadamosi Farouk 1 Jan 31, 2022
CVNets: A library for training computer vision networks

CVNets: A library for training computer vision networks This repository contains the source code for training computer vision models. Specifically, it

Apple 1.1k Jan 03, 2023
Code for paper "A Critical Assessment of State-of-the-Art in Entity Alignment" (https://arxiv.org/abs/2010.16314)

A Critical Assessment of State-of-the-Art in Entity Alignment This repository contains the source code for the paper A Critical Assessment of State-of

Max Berrendorf 16 Oct 14, 2022