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SMPL: A Skinned Multi-Person Linear Model

📅 August 1, 2023 👤 Matthew Loper, Naureen Mahmood, Javier Romero et al. 📖 ACM eBooks 📊 713 citations

🤖 Plain-English Summary

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.

🔑 Key Findings

  • 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.
  • 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.

💡 Why This Matters

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

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Aug 01, 2023
Journal ACM eBooks
Authors Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons‐Moll, Michael J. Black
DOI 10.1145/3596711.3596800
Citations 713
Source OpenAlex

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