SMPL is a realistic 3D model of the human body that is based on skinning and blend shapes and is learned from thousands of 3D body scans.
This site provides resources to learn about SMPL, including example FBX files with animated SMPL models, and code for using SMPL in Maya.
We will be rolling out support for more graphics software, more example animations, dynamic blend shapes, and SMPL for computer vision over the coming weeks.
Check out our news section.

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We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.



We released a version of SMPL for Python.
Check out the new update (v 1.0.2) of our FBX files and Maya plugin.
We have released a version of SMPL for Maya, which allows editing of body shape and pose. Also, you can now download sample animations that can be loaded in other packages like Blender or Unreal.
The SMPL website is now live! Register to keep getting updates by e-mail.

Referencing the Model

When using SMPL please reference:

      author = {Loper, Matthew and Mahmood, Naureen and Romero, Javier and Pons-Moll, Gerard and Black, Michael J.},
      title = {{SMPL}: A Skinned Multi-Person Linear Model},
      journal = {ACM Trans. Graphics (Proc. SIGGRAPH Asia)},
      month = oct,
      number = {6},
      pages = {248:1--248:16},
      publisher = {ACM},
      volume = {34},
      year = {2015}