smpl-model
There are 17 repositories under smpl-model topic.
YuliangXiu/ICON
[CVPR'22] ICON: Implicit Clothed humans Obtained from Normals
IDEA-Research/OSX
[CVPR 2023] Official implementation of the paper "One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer"
google/aistplusplus_api
API to support AIST++ Dataset: https://google.github.io/aistplusplus_dataset
qianlim/CAPE
Official implementation of CVPR2020 paper "Learning to Dress 3D People in Generative Clothing" https://arxiv.org/abs/1907.13615
muelea/shapy
CVPR 2022 - Official code repository for the paper: Accurate 3D Body Shape Regression using Metric and Semantic Attributes.
DavidBoja/SMPL-Anthropometry
Measure the SMPL body model
vcarlosrb/3d-body-measurements
Body measurements
Meshcapade/wiki
A Wiki on Body-Modelling Technology, maintained by Meshcapade GmbH.
gillet-thomas/smartwearables
A real time virtual try-on application using SMPL models and OpenCV
google/aistplusplus_dataset
AIST++ Dataset Webpage: https://google.github.io/aistplusplus_dataset
eliabntt/animated_human_SMPL_to_USD
Code used in the GRADE framework to convert SMPL animation data to the USD file format to be used in the IsaacSim/Omniverse simulators.
kristijanbartol/linear-3d-humans
"Linear Regression vs. Deep Learning". The source code for a simple but effective baseline method for human body measurement estimation using only height and weight information about the person.
SudeepRed/MatchUp_ELC
A way to visualize clothes on custom body measurements.
Arktische/smpl-cpp
A 100% compatiable SMPL,SMPL-H,SMPL-X model implemention in C++ with CUDA support. Same api with python smplx.
cameronking4/XperienceShopping-ChromeExtension
Enables users to create a profile saved only to local storage and get size recommendations across sites. This is the ultimate size recommendation plugin, saving time & money for online shoppers as it uses CV to predict body measurements and GPT 4 to provide size recommendations specific to a brand.
luismautone/human-body-mcf
Human 3D model partiality representation via Mean Curvature Flow