This repository implements VPoser, a variational autoencoder-based human pose prior for SMPL/X body models. It provides tools to encode/decode human poses into a low-dimensional latent space, sample novel poses, and perform inverse kinematics fitting to 2D/3D key points using the learned prior. The codebase includes data preparation scripts for the AMASS dataset, a PyTorch Lightning trainer, a flexible IK engine with LBFGS/Adam optimizers, and utilities for rotation mathematics and linear blend skinning. The system is designed as a reusable library for pose prior applications in research and graphics. Architecture-wise, the project is organized into modules: body_model (SMPL/X loading and LBS), models (VPoser VAE and IK engine), tools (rotation conversions, 6D representation, file/path helpers), data (dataset loading and preparation), train (training scripts), and tutorials (Jupyter notebooks and example scripts). Entry points include training scripts (e.g., train/V02_05/V02_05.py), IK demonstrations (tutorials/ik_example_*.py), and usage tutorials (tutorials/vposer.ipynb). External dependencies are primarily PyTorch, Lightning, OmegaConf, and optional visualization packages. Data flows: For training, AMASS .npz files are converted to .pt tensors via prepare_data.py, loaded by VPoserDS, and fed into VPoserTrainer. For inference, pretrained models are loaded via model_loader.py, enabling pose encoding/decoding or IK fitting. The IK engine iteratively optimizes pose, shape, and translation in the VPoser latent space to match target key points.