Human motion synthesis using Unity3D

Overview

Human motion synthesis using Unity3D

Prerequisite:

Software: amc2bvh.exe, Unity 2017, Blender.
Unity: RockVR (Video Capture), scenes, character models Files:
Motion files: amc, asf or bvh formats.
Character models: fbx format.

Procedure

  1. If motion files in amc/asf format, run amc2bvh.exe to convert them to bvh
  2. Place all bvh files into "Desktop/New folder/bvh" (or modify script)
  3. Open Blender and run the bvh2fbx.py script. It will convert the motion files to fbx format which Unity can process and place them under the unity "Resources/Input"[1]
  4. Find the imported motion file in Unity and change its Animation Type to Humanoid under Rig. Check to make sure the model is mapped properly.
  5. Configure the different variations to record video (characters, camera angle, scene, lighting)
    1. For characters, add[2] or remove from the "characters" GameObject in Unity Editor for the ones desired. For new character added to the scene, add the "New Animation Controller"[3] in Asset to the character's controller in the "Animator" section.
    2. For camera, change the position of the DedicatedCapture GameObjects to the desired location. Add additional DedicatedCapture GameObjects for more angle. Read the documentation for RockVR Video Capture for more detail.
    3. For scene, check the desired scenes within the intro scene and run.
    4. For lighting, change the "lights" parameter in Automation.cs script. Add more values to the array for more variations in lighting angles.
  6. Start up the "intro" scene and run it from Unity Editor. Click "Start" button to start the problem.
  7. Adjust the desired resolution and framerate and click start. For initial run, leave all the counters to 0. For continuing runs enter the counters where the previous run left off. The videos will be recorded to "Documents/RockVR/Video"[4]

Note

  • [1] Converting too many bvh files at a time may result in Blender crashing. Try converting them in batches of smaller quantity (~50).
  • [2] To add a GameObject to a Scene in Unity, drag it from the Asset menu to a position in the Hierarchy menu or a position in the scene itself. You can also create an empty GameObject from the "GameObject->Create Empty" option.
  • [3] Depending on the framerate of the motion files, you may need to adjust the speed of the animation. To do this go to "Assets" and find the "New Animator Controller" and open it. Then click on "New State" and adjust the speed to framerate/24 (if 120 frames changes to 5, if 60 change to 2.5, etc). Also find the line "timeLeft = ((AnimationClip)clips[clipCounter]).length;" in the SwitchAnimation function and divide it by the speed.
  • [4] Unity will most likely freeze or crash if left running for too long. Adjust the counters in the "intro" scene to resume progress.

Scene Creation procedure

  1. To get a scene, either download a pre-built one or build one yourself using various 3d models for GameObjects.
  2. Create an empty GameObject named "characters" and place it at a location best suited for recording. Add a character to it to see if any adjusting or scaling is needed.
  3. Add DedicatedCapture GameObjects from the "RockVR/Video/Prefabs" folder to the scene in desired locations.
  4. Attach the AudioCapture script in "RockVR/Video/Scripts" folder to the main camera.
  5. Create an empty GameObject named "VideoCaptureCtrl" and attach the VideoCaptureCtrl script in "RockVR/Video/Scripts" to it. Also attach the Automation.cs script from "Scripts" to it as well.
  6. Add the first DedicatedCapture GameObject as well as the AudioCapture to the the VideoCaptureCtrl script.
  7. If there is no "Directional light" GameObject, create one.
  8. Add the created scene to build settings.
  9. Add a check box in the intro scene for the newly created scene and modify the scene "ProcessParameter" accordingly.

Additional characters

In the "characters" folder in Assets, there is a list of preprocessed characters I got from the Unity asset store for free.
To process new characters:

  1. Change its Animation type to Humanoid under Rig
  2. Fix any mapping problem for the bones of the character
  3. Remove the mapping on the bones for both hands. This could be done using the "New Human Template" in the Assets folder. (This is to avoid weird finger mapping from the animations)

Instructions on error handling

  • If you tried to terminate the program insider the Unity Editor, the ffmpeg.exe will still be running and result in unfinished video and audio files to remain in the videos folder. To solve this issue, simply terminate the ffmpeg.exe from task manager and delete the unfinished files.
  • Since the program freezes fairly often, a temporary save state feature is implemented. Once Unity froze, terminate it from task manager. Look into the videos folder and figure out what combination the next video should be. Enter the parameters where the last run left off in the "intro" scene (various counters) to pick up from there.

Local environment specs

  • OS: Microsoft Windows 10 Pro
  • Version: 10.0.16299 Build 16299
  • Processor: Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz, 2201 Mhz, 10 Core(s), 20 Logical Processor(s)
  • Total Physical Memory: 63.9 GB
  • GPU: NVIDIA Quadro M5000
Owner
Hao Xu
Hao Xu
Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

An official implementation of paper Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

11 Nov 23, 2022
TensorFlow-LiveLessons - "Deep Learning with TensorFlow" LiveLessons

TensorFlow-LiveLessons Note that the second edition of this video series is now available here. The second edition contains all of the content from th

Deep Learning Study Group 830 Jan 03, 2023
1st place solution in CCF BDCI 2021 ULSEG challenge

1st place solution in CCF BDCI 2021 ULSEG challenge This is the source code of the 1st place solution for ultrasound image angioma segmentation task (

Chenxu Peng 30 Nov 22, 2022
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

Keon Lee 140 Dec 21, 2022
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-dri

Neural Magic 1.5k Dec 30, 2022
Official PyTorch implementation of "Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble" (NeurIPS'21)

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble This is the code for reproducing the results of the paper Uncertainty-Bas

43 Nov 23, 2022
Set of models for classifcation of 3D volumes

Classification models 3D Zoo - Keras and TF.Keras This repository contains 3D variants of popular CNN models for classification like ResNets, DenseNet

69 Dec 28, 2022
Code for the CVPR2022 paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity"

Introduction This is an official release of the paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity" (arxiv link). Abstrac

Leo 21 Nov 23, 2022
Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out) created with Python.

Hand Gesture Volume Controller Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out). Code Firstly I have created a

Tejas Prajapati 16 Sep 11, 2021
The implementation of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information".

The HIST framework for stock trend forecasting The implementation of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining C

Wentao Xu 110 Dec 27, 2022
Self-supervised spatio-spectro-temporal represenation learning for EEG analysis

EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation This repository provides a tensorflow implementation of a submitted paper: EEG-Orie

Wonjun Ko 4 Jun 09, 2022
An imperfect information game is a type of game with asymmetric information

DecisionHoldem An imperfect information game is a type of game with asymmetric information. Compared with perfect information game, imperfect informat

Decision AI 25 Dec 23, 2022
Expert Finding in Legal Community Question Answering

Expert Finding in Legal Community Question Answering Arian Askari, Suzan Verberne, and Gabriella Pasi. Expert Finding in Legal Community Question Answ

Arian Askari 3 Oct 31, 2022
Code for CVPR2021 paper "Learning Salient Boundary Feature for Anchor-free Temporal Action Localization"

AFSD: Learning Salient Boundary Feature for Anchor-free Temporal Action Localization This is an official implementation in PyTorch of AFSD. Our paper

Tencent YouTu Research 146 Dec 24, 2022
The code for the CVPR 2021 paper Neural Deformation Graphs, a novel approach for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.

Neural Deformation Graphs Project Page | Paper | Video Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction Aljaž Božič, Pablo P

Aljaz Bozic 134 Dec 16, 2022
Research using Cirq!

ReCirq Research using Cirq! This project contains modules for running quantum computing applications and experiments through Cirq and Quantum Engine.

quantumlib 230 Dec 29, 2022
Fast EMD for Python: a wrapper for Pele and Werman's C++ implementation of the Earth Mover's Distance metric

PyEMD: Fast EMD for Python PyEMD is a Python wrapper for Ofir Pele and Michael Werman's implementation of the Earth Mover's Distance that allows it to

William Mayner 433 Dec 31, 2022
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions

Natural Posterior Network This repository provides the official implementation o

Oliver Borchert 54 Dec 06, 2022
Official implementation for "Symbolic Learning to Optimize: Towards Interpretability and Scalability"

Symbolic Learning to Optimize This is the official implementation for ICLR-2022 paper "Symbolic Learning to Optimize: Towards Interpretability and Sca

VITA 8 Dec 19, 2022
Complete* list of autonomous driving related datasets

AD Datasets Complete* and curated list of autonomous driving related datasets Contributing Contributions are very welcome! To add or update a dataset:

Daniel Bogdoll 13 Dec 19, 2022