3D Human Pose Estimation

138 papers with code • 11 benchmarks • 20 datasets

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Greatest papers with code

XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera

rwightman/pytorch-image-models 1 Jul 2019

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.

Monocular 3D Human Pose Estimation Motion Capture

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

facebookresearch/pifuhd CVPR 2020

Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images.

3D Human Pose Estimation 3D Human Shape Estimation +2

End-to-end Recovery of Human Shape and Pose

open-mmlab/mmpose CVPR 2018

The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations.

3D Multi-Person Pose Estimation Monocular 3D Human Pose Estimation +1

A simple yet effective baseline for 3d human pose estimation

open-mmlab/mmpose ICCV 2017

Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels.

3D Pose Estimation Monocular 3D Human Pose Estimation

Human3.6m: Large scale datasets and predictive methods for 3D human sensing in natural environments

open-mmlab/mmpose 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.

3D Human Pose Estimation Mixed Reality +1

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

shunsukesaito/PIFu ICCV 2019

We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object.

3D Human Pose Estimation 3D Object Reconstruction From A Single Image +1