awesome-human-motion

3D HourGlass Networks for Human Pose Estimation Through Videos.

A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.

A simple baseline for 3d human pose estimation in PyTorch.

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera.

The authors' implementation of Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations.

3D Human Pose Machines with Self-supervised Learning.

This repository contains the open source code which reproduces the results for the paper: Adversarial learning for modeling human motion.

Implementation of Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis.

A Re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing".

Tensorflow Implementation of ECCV'18 paper: Multimodal Human Motion Synthesis.

An LSTM based GAN for Human motion synthesis.

Human motion synthesis using Unity3D.

R&D Engineer and ML Enthusiast.

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.

In this project, we investigate musculoskeletal modeling and simulation to analyze and understand human movement and performance. Our objective is to reconstruct human motion from measurement data for example for medical assessments or to predict human responses for virtual product development.

Deep Learning for Technical Artists in Animation, VFX, and Games.

We combine research on computer vision, computer graphics, and machine learning to teach computers to see us and by understanding our behavior learn to be more human.

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.

[📷🎥🎤🤾‍♀️⌚️🤹‍♀️][👨‍🦰👩][👧👵] - 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.

This is the project page for creating a database of interactions between a character and an object/objects.

Carnegie Mellon University Motion Capture Database

[📷][👨‍🦰👩][👧👵] - COCO is a large-scale object detection, segmentation, and captioning dataset.

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.

Large Scale Datasets and Predictive Methodsfor 3D Human Sensing in Natural Environments

Look up the rank of your conference.

The Computer Vision Foundation

Top publications by category and/or language.

International Conference on Robotics and Automation

Medical Image Computing and Computer-Assisted Intervention

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.

Qinematic has developed software for 3D markerless motion capture and human movement analysis since 2012.

wrnch is a computer vision / deep learning software engineering company based in Montréal, Canada, a renowned hub for AI.

Tools for the Cleveland State Human Motion and Control Lab.

A motion detection system with RaspberryPi, OpenCV, Python.

Experiments in classifying human actions using the UCF action databased.

This project is for visualising human motion data captured from Kinect V2 for further data analysis.