3D human pose and shape estimation

27 papers with code • 1 benchmarks • 2 datasets

Estimate 3D human pose and shape (e.g. SMPL) from images

Most implemented papers

CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation

huawei-noah/noah-research 1 Aug 2022

Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem.

HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation

Jeff-sjtu/HybrIK CVPR 2021

We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods.

Unite the People: Closing the Loop Between 3D and 2D Human Representations

MandyMo/pytorch_HMR CVPR 2017

With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body.

Convolutional Mesh Regression for Single-Image Human Shape Reconstruction

nkolot/GraphCMR CVPR 2019

Image-based features are attached to the mesh vertices and the Graph-CNN is responsible to process them on the mesh structure, while the regression target for each vertex is its 3D location.

3D Human Pose, Shape and Texture from Low-Resolution Images and Videos

xuxy09/RSC-Net 11 Mar 2021

Two common approaches to deal with low-resolution images are applying super-resolution techniques to the input, which may result in unpleasant artifacts, or simply training one model for each resolution, which is impractical in many realistic applications.

PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop

HongwenZhang/PyMAF ICCV 2021

Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images.

AnthroNet: Conditional Generation of Humans via Anthropometrics

Unity-Technologies/com.unity.cv.synthetichumans 7 Sep 2023

We present a novel human body model formulated by an extensive set of anthropocentric measurements, which is capable of generating a wide range of human body shapes and poses.

Learning 3D Human Shape and Pose from Dense Body Parts

HongwenZhang/DaNet-DensePose2SMPL 31 Dec 2019

Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods.

HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation

redrock303/HEMlets 10 Mar 2020

Estimating 3D human pose from a single image is a challenging task.

Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data

Healthcare-Robotics/bodies-at-rest CVPR 2020

We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes.