temporal_inverse_kinematics
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khanhha
a learning-based human inverse kinemtics. A graph convolution network is constructed to predict SMPLx joint angles from a tepmoral sequence of relative 3d poses in COCO format.
Deep learning-based inverse kinematics
Introduction
This project presents a human inverse kinemtics solution based on deep learning. A graph convolution network is constructed to predict SMPLx joint angles from a tepmoral sequence of relative 3d poses in COCO format.
Install
conda env create -n motion python=3.8.2
conda activate motion
pip install -r requirements.txt
prepare the dataset
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register and download the Amass dataset, save it to the folder AMASS_DIR
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register and download the SMPLx models, save it also to the folder AMASS_DIR
Run test inference.
download the amass dataset, SMPLx models and save it to the folder AMASS_DIR
python inference.py ./data/sample_3d_poses/dance_contemporary.npz AMASS_DIR
Train the model
python ./model_wrap.py --amass AMASS_DIR --data_dir DIR_TO_SAVE_MODELS --smpl_mean ./data/smpl/smpl_mean_params.npz
khanhha