Human Mesh Recovery
39 papers with code • 0 benchmarks • 2 datasets
Estimate 3D body mesh from images
Benchmarks
These leaderboards are used to track progress in Human Mesh Recovery
MoVi
FLAG3D
Most implemented papers
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.
Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation
Arthur151/DSD-SATN
•
•
ICCV 2019
Different from the existing methods try to obtain all the complex 3D pose, shape, and camera parameters from one coupling feature, we propose a skeleton-disentangling based framework, which divides this task into multi-level spatial and temporal granularity in a decoupling manner.
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.
LiDAR-HMR: 3D Human Mesh Recovery from LiDAR
In recent years, point cloud perception tasks have been garnering increasing attention.
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Human-centric perception tasks, e. g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis.
Learning 3D Human Shape and Pose from Dense Body Parts
HongwenZhang/DaNet-DensePose2SMPL
•
•
Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods.
Human Mesh Recovery from Multiple Shots
geopavlakos/multishot
•
•
CVPR 2022
The tools we develop open the door to processing and analyzing in 3D content from a large library of edited media, which could be helpful for many downstream applications.
Self-supervised 3D Human Mesh Recovery from Noisy Point Clouds
wangsen1312/unsupervised3dhuman
•
•
However, Chamfer distance is quite sensitive to noise and outliers, thus could be unreliable to assign correspondences.
Probabilistic Modeling for Human Mesh Recovery
This paper focuses on the problem of 3D human reconstruction from 2D evidence.
Leveraging MoCap Data for Human Mesh Recovery
naver/posebert
•
•
In fact, we show that simply fine-tuning the batch normalization layers of the model is enough to achieve large gains.
