SMPL: A Skinned Multi-Person Linear Model
SMPL: A Skinned Multi-Person Linear Model
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DOI：
10.1145/2816795.2818013
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发表时间：
2015-11-01
影响因子：
6.2
通讯作者：
Black, Michael J.
中科院分区：
文献类型：
--
作者：
Loper, Matthew;Mahmood, Naureen;Black, Michael J.
我们提出了一个人体形状和姿势相关的形状变化的学习模型，该模型比以前的模型更准确，并且与现有的图形管道兼容。我们的蒙皮多人线性模型(SMPL)是一个基于蒙皮的顶点模型，可以准确地表示自然人体姿势中的各种身体形状。模型的参数从数据中学习，包括静止姿势模板、混合权重、姿势相关的混合变形、身份相关的混合变形以及从顶点到关节位置的回归。与以前的模型不同，姿势相关的混合变形是姿势旋转矩阵元素的线性函数。这个简单的公式能够从相对大量的不同姿势的不同人的对齐3D网格中训练整个模型。我们使用线性或双四元数混合蒙皮来定量评估SMPL的变体，并表明这两种模型都比基于相同数据训练的Blend-SCAPE模型更准确。我们还扩展了SMPL以逼真地模拟动态软组织变形。因为它基于混合蒙皮，所以SMPL与现有的渲染引擎兼容，我们将其用于研究目的。
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.