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Mark Pauly
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 146
Citations - 15865
Mark Pauly is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Computer facial animation & Polygon mesh. The author has an hindex of 61, co-authored 144 publications receiving 14265 citations. Previous affiliations of Mark Pauly include Indian Institute of Technology Delhi & ETH Zurich.
Papers
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Proceedings ArticleDOI
Efficient simplification of point-sampled surfaces
TL;DR: This work has implemented incremental and hierarchical clustering, iterative simplification, and particle simulation algorithms to create approximations of point-based models with lower sampling density, and shows how local variation estimation and quadric error metrics can be employed to diminish the approximation error.
Book
Polygon Mesh Processing
TL;DR: This chapter discusses the development of types of Repair Algorithms for Surface Definition and Properties Approximation andParameterization of a Triangulated Surface Barycentric Mapping.
Proceedings ArticleDOI
Realtime performance-based facial animation
TL;DR: A novel face tracking algorithm that combines geometry and texture registration with pre-recorded animation priors in a single optimization is introduced that demonstrates that compelling 3D facial dynamics can be reconstructed in realtime without the use of face markers, intrusive lighting, or complex scanning hardware.
Proceedings ArticleDOI
Embedded deformation for shape manipulation
TL;DR: An algorithm that generates natural and intuitive deformations via direct manipulation for a wide range of shape representations and editing scenarios through direct manipulation of objects embedded within it, while preserving the embedded objects' features.
Journal ArticleDOI
Multi-scale Feature Extraction on Point-Sampled Surfaces
TL;DR: Central to the method is a multi‐scale classification operator that allows feature analysis at multiplescales, using the size of the local neighborhoods as a discrete scale parameter, which significantly improves thereliability of the detection phase and makes the method more robust in the presence of noise.