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awesome-human-motion's Introduction

Awesome Human Motion Awesome

πŸƒβ€β™€οΈ A curated list about human motion capture, analysis and synthesis.

Contents

Introduction

Human Models

  • SMPL - SMPL is a realistic 3D model of the human body that is based on skinning and blend shapes and is learned from thousands of 3D body scans.
  • MakeHuman - MakeHuman is an open source (AGPL3) tool designed to simplify the creation of virtual humans using a Graphical User Interface, also commonly referred to as a GUI.

Datasets

  • Human 3.6M - Large Scale Datasets and Predictive Methodsfor 3D Human Sensing in Natural Environments
  • SURREAL - Learning from Synthetic Humans, CVPR 2017
  • CMU - Carnegie Mellon University Motion Capture Database
  • Berkley MHAD - [πŸ“·πŸŽ₯πŸŽ€πŸ€Ύβ€β™€οΈβŒšοΈπŸ€Ήβ€β™€οΈ][πŸ‘¨β€πŸ¦°πŸ‘©][πŸ‘§πŸ‘΅] - The Berkeley Multimodal Human Action Database (MHAD) contains 11 actions performed by 7 male and 5 female subjects in the range 23-30 years of age except for one elderly subject.
  • COCO - [πŸ“·][πŸ‘¨β€πŸ¦°πŸ‘©][πŸ‘§πŸ‘΅] - COCO is a large-scale object detection, segmentation, and captioning dataset.
  • HDM05 - HDM05 contains more than three hours of systematically recorded and well-documented motion capture data in the C3D as well as in the ASF/AMC data format.
  • KIT Whole-Body Human Motion Database
  • CGVU Interaction Database - This is the project page for creating a database of interactions between a character and an object/objects.

Description

  • Sensors and Data Types - πŸ“·(image), πŸŽ₯(video), 🎀(audio), πŸ€Ύβ€β™€οΈ(Motion Capture), ⌚️(IMU or wearables), πŸ€Ήβ€β™€οΈ(Kinect or similar)
  • Sex - πŸ‘¨β€πŸ¦°(male), πŸ‘©(female)
  • Age - πŸ‘§(young), πŸ‘΅(eldery)

Data Processing

Recording

Data Conversion

  • video to bvh - Convert human motion from video to .bvh.
  • MotionCapturePy - Converts motion capture data from ASF and AMC files to Cartesian numpy arrays in python. Also plots a moving human frame using matplotlib.

Misc

Pose Estimation

Lectures

Papers

Implementations

  • 3Dpose_ssl - 3D Human Pose Machines with Self-supervised Learning.
  • 3dpose_gan - The authors' implementation of Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations.
  • 3d_pose_baseline_pytorch - A simple baseline for 3d human pose estimation in PyTorch.
  • 3d-pose-estimation - VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera.
  • 3D-HourGlass-Network - 3D HourGlass Networks for Human Pose Estimation Through Videos.
  • adversarially_parameterized_optimization - GAN-based 3D human pose estimation.
  • DensePose - A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body
  • VideoPose3D - Efficient 3D human pose estimation in video using 2D keypoint trajectorie.
  • 3d-pose-baseline - A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.
  • Human Shape and Pose - End-to-end Recovery of Human Shape and Pose - CVPR 2018
  • AlphaPose - Real-Time and Accurate Multi-Person Pose Estimation&Tracking System.

Motion Analysis

Implementations

Motion Synthesis

Implementations

Researchers, Institutes, Projects

People

  • Daniel Holden - My name is Daniel Holden. I'm a programmer, writer, and digital artist currently working as a Machine Learning researcher at Ubisoft Montreal. My interests are Computer Graphics, Game Development, Theory of Computation, and Programming Languages.
  • Gustavo Boehs (3DeepLearner) - Deep Learning for Technical Artists in Animation, VFX, and Games.
  • Arash Hosseini - R&D Engineer and ML Enthusiast.
  • Sebastian Starke - Ph.D. student in Character Animation and Artificial Intelligence at the University of Edinburgh, School of Informatics, Institute of Perception, Action and Behaviour, supervised by Dr. Taku Komura.

Institutes and Projects

Commercial Projects

  • wrnch.ai - wrnch is a computer vision / deep learning software engineering company based in MontrΓ©al, Canada, a renowned hub for AI.
  • Qinematic - Qinematic has developed software for 3D markerless motion capture and human movement analysis since 2012.
  • DeepMotion - DeepMotion’s solutions bridge physical and digital motion for virtual characters and machines. Using physics simulation, computer vision, and machine learning, DeepMotion reconstructs realistic motion from real-world examples.

Journals

Journal Ranking

Conferences

  • ICRA - International Conference on Robotics and Automation
  • MICCAI - Medical Image Computing and Computer-Assisted Intervention
  • CVF - The Computer Vision Foundation

Conference Ranking

Videos

Two Minute Papers

CVPR 2019

Credits

This list benefits massively from the research work of Loreen Pogrzeba.

Contribute

Contributions welcome! Read the contribution guidelines first.

License

CC0

To the extent possible under law, derikon has waived all copyright and related or neighboring rights to this work.

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awesome-human-motion's Issues

Interest to contact you and know further of applications about human motion

First of all @derikon, congrats on your amazing work!

I've gone trhough all what you have put together and it is super useful.

I would like to contact you if it is possible, to know a bit more of what it could be achieved with human motion.

I'm curious because I would like to train a video dataset of multi-person and see if it can translate, maybe first with SMPL to get the motion to after add human shape (with biggest accuracy) providing images (if available) and some default human appearance.

Let me know if you are available. Super interested on this topic and hope you can provide me some guidance.

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