Awesome
NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets
Introduction
This repo provides 3D pseudo-GTs (SMPL/MANO/FLAME/SMPL-X parameters) of various datasets, obtained by NeuralAnnot (CVPRW 2022 Oral.). We additionally provide 3D pseudo-GTs of SMPL parameters of MSCOCO, obtained by Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild (CVPRW 2023). You need to install smplx.
Human3.6M
MPI-INF-3DHP
MSCOCO
- [data]
- [SMPL parameters]
- [MANO parameters]
- [FLAME parameters]
- [SMPL-X parameters (whole body)]
- [SMPL parameters from Three Recipes]
- [SMPL parameters from Two Recipes (without using 3DPW training set)]
MPII 2D Pose Dataset
3DPW
CrowdPose
FFHQ
- [data]
- [FLAME parameters]
InstaVariety
- [data]
- [SMPL parameters]
InterHand2.6M
Reference
@InProceedings{Moon_2022_CVPRW_NeuralAnnot,
author = {Moon, Gyeongsik and Choi, Hongsuk and Lee, Kyoung Mu},
title = {NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets},
booktitle = {Computer Vision and Pattern Recognition Workshop (CVPRW)},
year = {2022}
}
@InProceedings{Moon_2023_CVPRW_3Dpseudpgts,
author = {Moon, Gyeongsik and Choi, Hongsuk and Chun, Sanghyuk and Lee, Jiyoung and Yun, Sangdoo},
title = {Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild},
booktitle = {Computer Vision and Pattern Recognition Workshop (CVPRW)},
year = {2023}
}