Crowd-sourced Annotation of Human Motion.

Overview

Motion Annotation Tool

Installation

Start by installing all Python dependencies:

pip install -r requirements.txt

Next, install SRILM. You'll have to adapt the path to SRILM in src/proj/settings.py. You might also have to adapt other parts of the configuration depending on your needs. You should also make sure that your configuration is secure. Please consult the Django documentation for this!

Finally, you can set up the database:

cd src/
python manage.py migrate
python manage.py createsuperuser

At this point everything is ready. However, you'll probably need some motion data:

python manage.py importmotions

This step requires a free account for the KIT Whole-Body Human Motion Database. You can select different filters. At this point, only a single subject can be visualized so you should at least set the maximum number of subjects to 1. This step is going to take a while. After all motions have been imported, you might have to collect the static files and switch them to visible:

python manage.py collectstatic
python manage.py dbshell
UPDATE dataset_motionfile SET is_hidden=0;

Lastly, you can try if everything works by running a local server:

python manage.py runserver

Just visit http://localhost:8000. If everything worked, you should be able to log in using your previously created account.

Owner
Matthias Plappert
I am a research scientist working on machine learning, and especially deep reinforcement learning, in robotics.
Matthias Plappert
Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

📖 Depth-Aware Generative Adversarial Network for Talking Head Video Generation (CVPR 2022) 🔥 If DaGAN is helpful in your photos/projects, please hel

Fa-Ting Hong 503 Jan 04, 2023
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official

zhaohu xing 112 Dec 16, 2022
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".

Rule-based Representation Learner This is a PyTorch implementation of Rule-based Representation Learner (RRL) as described in NeurIPS 2021 paper: Scal

Zhuo Wang 53 Dec 17, 2022
EPSANet:An Efficient Pyramid Split Attention Block on Convolutional Neural Network

EPSANet:An Efficient Pyramid Split Attention Block on Convolutional Neural Network This repo contains the official Pytorch implementaion code and conf

Hu Zhang 175 Jan 07, 2023
Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022

AMRBART An implementation for ACL2022 paper "Graph Pre-training for AMR Parsing and Generation". You may find our paper here (Arxiv). Requirements pyt

xfbai 60 Jan 03, 2023
3D Human Pose Machines with Self-supervised Learning

3D Human Pose Machines with Self-supervised Learning Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, and Pengxu Wei, “3D Human Pose Machines with Self

Chenhan Jiang 398 Dec 20, 2022
This is the official implementation of our proposed SwinMR

SwinMR This is the official implementation of our proposed SwinMR: Swin Transformer for Fast MRI Please cite: @article{huang2022swin, title={Swi

A Yang Lab (led by Dr Guang Yang) 27 Nov 17, 2022
StyleGAN2 - Official TensorFlow Implementation

StyleGAN2 - Official TensorFlow Implementation

NVIDIA Research Projects 10.1k Dec 28, 2022
A toolkit for Lagrangian-based constrained optimization in Pytorch

Cooper About Cooper is a toolkit for Lagrangian-based constrained optimization in Pytorch. This library aims to encourage and facilitate the study of

Cooper 34 Jan 01, 2023
Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th place solution

Lyft Motion Prediction for Autonomous Vehicles Code for the 4th place solution of Lyft Motion Prediction for Autonomous Vehicles on Kaggle. Discussion

44 Jun 27, 2022
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
End-To-End Crowdsourcing

End-To-End Crowdsourcing Comparison of traditional crowdsourcing approaches to a state-of-the-art end-to-end crowdsourcing approach LTNet on sentiment

Andreas Koch 1 Mar 06, 2022
Official pytorch implementation of DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces

DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces Minhyuk Sung*, Zhenyu Jiang*, Panos Achlioptas, Niloy J. Mitra, Leonidas

Zhenyu Jiang 21 Aug 30, 2022
Pre-training of Graph Augmented Transformers for Medication Recommendation

G-Bert Pre-training of Graph Augmented Transformers for Medication Recommendation Intro G-Bert combined the power of Graph Neural Networks and BERT (B

101 Dec 27, 2022
Syllabus del curso IIC2115 - Programación como Herramienta para la Ingeniería 2022/I

IIC2115 - Programación como Herramienta para la Ingeniería Videos y tutoriales Tutorial CMD Tutorial Instalación Python y Jupyter Tutorial de git-GitH

21 Nov 09, 2022
This is the official pytorch implementation of Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation(TESKD)

Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation (TESKD) By Zheng Li[1,4], Xiang Li[2], Lingfeng Yang[2,4], Jian Yang[2], Zh

Zheng Li 9 Sep 26, 2022
Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch

CoCa - Pytorch Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch. They were able to elegantly fit in contras

Phil Wang 565 Dec 30, 2022
Computer Vision Script to recognize first person motion, developed as final project for the course "Machine Learning and Deep Learning"

Overview of The Code BaseColab/MLDL_FPAR.pdf: it contains the full explanation of our work Base Colab: it contains the base colab used to perform all

Simone Papicchio 4 Jul 16, 2022
Funnels: Exact maximum likelihood with dimensionality reduction.

Funnels This repository contains the code needed to reproduce the experiments from the paper: Funnels: Exact maximum likelihood with dimensionality re

2 Apr 21, 2022
Action Segmentation Evaluation

Reference Action Segmentation Evaluation Code This repository contains the reference code for action segmentation evaluation. If you have a bug-fix/im

5 May 22, 2022