Motion Capture
109 papers with code • 0 benchmarks • 18 datasets
Benchmarks
SURREAL
AMASS
TotalCapture
MoVi
GRAB
HIC
xR-EgoPose
EgoCap
Subtasks
Greatest papers with code
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
CMU-Perceptual-Computing-Lab/openpose
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CVPR 2017
The method is used to train a hand keypoint detector for single images.
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
google-research/google-research
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CVPR 2020
We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera; and second, we develop a deep neural network, called $KeyPose$, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects.
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
rwightman/pytorch-image-models
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The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
Ranked #3 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
Local motion phases for learning multi-contact character movements
sebastianstarke/AI4Animation
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10/06 2020
Training a bipedal character to play basketball and interact with objects, or a quadruped character to move in various locomotion modes, are difficult tasks due to the fast and complex contacts happening during the motion.
VIBE: Video Inference for Human Body Pose and Shape Estimation
Human motion is fundamental to understanding behavior.
Ranked #12 on
3D Human Pose Estimation
on 3DPW
(using extra training data)
Unsupervised learning with sparse space-and-time autoencoders
facebookresearch/SparseConvNet
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We use spatially-sparse two, three and four dimensional convolutional autoencoder networks to model sparse structures in 2D space, 3D space, and 3+1=4 dimensional space-time.
FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration
facebookresearch/frankmocap
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To construct FrankMocap, we build the state-of-the-art monocular 3D "hand" motion capture method by taking the hand part of the whole body parametric model (SMPL-X).
Human3.6m: Large scale datasets and predictive methods for 3D human sensing in natural environments
open-mmlab/mmpose
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IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 36 , Issue: 7 , July 2014 ) 2013
We introduce a new dataset, Human3. 6M, of 3. 6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms.
Ranked #69 on
3D Human Pose Estimation
on Human3.6M
Scalable Gradients for Stochastic Differential Equations
The adjoint sensitivity method scalably computes gradients of solutions to ordinary differential equations.
Ranked #1 on
Video Prediction
on CMU Mocap-2
Skeleton-Aware Networks for Deep Motion Retargeting
DeepMotionEditing/deep-motion-editing
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In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons.