Motion Capture
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Greatest papers with code
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
CVPR 2017
• CMU-Perceptual-Computing-Lab/openpose
• 
The method is used to train a hand keypoint detector for single images.
XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera
• osmr/imgclsmob
• 
In the second stage, a fully-connected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual.
#8 best model for
3D Human Pose Estimation
on MPI-INF-3DHP
VIBE: Video Inference for Human Body Pose and Shape Estimation
• mkocabas/VIBE
•
Human motion is fundamental to understanding behavior.
SOTA for 3D Human Pose Estimation on 3DPW (using extra training data)
Neural Relational Inference for Interacting Systems
Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics.
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
CVPR 2019
• CMU-Perceptual-Computing-Lab/MonocularTotalCapture
• 
We present the first method to capture the 3D total motion of a target person from a monocular view input.
3D HUMAN POSE ESTIMATION HAND POSE ESTIMATION MOTION CAPTURE
Learning from Synthetic Humans
CVPR 2017
• gulvarol/surreal
• 
In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.
The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight
The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale, aggressive indoor flight dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception. Inspired by the potential of future high-speed fully-autonomous drone racing, the Blackbird dataset contains over 10 hours of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to $7. 0ms^-1$.
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements.
Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data
We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy.
Marathon Environments: Multi-Agent Continuous Control Benchmarks in a Modern Video Game Engine
Recent advances in deep reinforcement learning in the paradigm of locomotion using continuous control have raised the interest of game makers for the potential of digital actors using active ragdoll.