SMPL: A Skinned Multi-Person Linear Model

We present a learned model of human body shape and posedependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. We also extend SMPL to realistically model dynamic soft-tissue deformations.

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• 1 Our Skinned Multi-Person Linear model (SMPL) is a skinned vertexbased model that accurately represents a wide variety of body shapes in natural human poses.
• 2 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.
• 3 Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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