Towards Statistically Provable Geometric 3D Human Pose Recovery

DOI10.1137/19M1299955zbMath1492.68140OpenAlexW3132218418MaRDI QIDQ5860283

Dahua Lin, Ji Liu, Jianqiao Wangni, Kostas Daniilidis, Jianbo Shi

Publication date: 19 November 2021

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/19m1299955

zbMATH Keywords

nonconvex optimizationdeep learning3D recoverysparse modelhuman pose

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Applications of mathematical programming (90C90) Nonconvex programming, global optimization (90C26) Computing methodologies for image processing (68U10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05)

Uses Software

• SMPL
• MS-COCO
• MonoCap
• Human3.6M
• CMU Motion Capture
• MOCAP

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Statistics for high-dimensional data. Methods, theory and applications.
• The restricted isometry property and its implications for compressed sensing
• Multi-stage convex relaxation for feature selection
• Generalized power method for sparse principal component analysis
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• High-Dimensional Statistics
• Smoothly Clipped Absolute Deviation on High Dimensions
• Computer vision. Algorithms and applications

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