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SMPL

C++ Implementation of SMPL - A Skinned Multi-Person Linear Model.

Overview

This project implements a 3D human skinning model - SMPL: A Skinned Multi-Person Linear Model with C++ and pure CUDA. The official SMPL model is available at http://smpl.is.tue.mpg.de.

For more details, see the paper published by Max Planck Institute for Intelligent Systems on SIGGRAPH ASIA 2015.

Prerequisites

  1. xtensor: A C++ library meant for numerical analysis with multi-dimensional array expressions.

  2. nlohmann_json: JSON for Modern C++.

    Xtensor loads data from and dumps data into JSONs through nlohmann's toolkit.

  3. CUDA: NVIDIA parallel computing platform.

Usage

  • Data preprocessing

    Download and extract the official data from http://smpl.is.tue.mpg.de/, you will get two files:

    basicModel_f_lbs_10_207_0_v1.0.0.pkl
basicmodel_m_lbs_10_207_0_v1.0.0.pkl

    Run preprocess.py in SMPL with Python 2 to convert the data into .json and . npz format.

  • Build and Run

    After installing all packages, you can compile SMPL from source:

    mkdir build
cd build
cmake ..
make

  • Pipeline

    Following the paper, we can generate a mesh with four steps.

    1. Generate pose blend shape and shape blend shape.

    2. Regress joints from vertices.

    3. Compute the transformation matrices for each joint.

    4. Linear Blend Skinning

Reference

[1] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. 2015. "SMPL: a skinned multi-person linear model". ACM Trans. Graph. 34, 6, Article 248 (October 2015), 16 pages.