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SMPL: a skinned multi-person linear model

Authors:

Naureen Mahmood,

Gerard Pons-Moll,

Michael J. BlackAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 34, Issue 6

Article No.: 248, Pages 1 - 16

https://doi.org/10.1145/2816795.2818013

Published: 26 October 2015 Publication History

Abstract

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.

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Index Terms

SMPL: a skinned multi-person linear model
1. Computing methodologies
  1. Computer graphics
    1. Animation

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 34, Issue 6

November 2015

944 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2816795

Issue’s Table of Contents

Seminal Graphics Papers: Pushing the Boundaries, Volume 2
August 2023
893 pages
ISBN:9798400708978
DOI:10.1145/3596711
Editor:
Mary C. Whitton
Department of Computer Science, UNC Chapel Hill, USA

Copyright © 2015 Owner/Author.

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Published: 26 October 2015

Published in TOG Volume 34, Issue 6

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https://doi.org/10.7717/peerj-cs.2574
Liu JWu JJing RYu HLiu JSong L(2025)Lightweight Explicit 3D Human Digitization via Normal IntegrationSensors10.3390/s2505151325:5(1513)Online publication date: 28-Feb-2025
https://doi.org/10.3390/s25051513
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