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Automatic Posing of a MeshedHuman Model Using Point Clouds
Lei Wang
Joint work with Tamal K. Dey,
Huamin Wang and Bo Fu
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Problem Statement
•Low-end scanning devices are becomingpopular. But quality of their output…
•Reconstruct from problematic human scans bydeforming a prior high quality template mesh
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Related Work
•Model-based Registration
•Parametric human body models
•Registration without a Model
•Manual intervention
•Non-rigid registration by nonlinear optimization
•Isometric deformations: keep geodesic distanceunchanged
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Our Method: Outline
computingcorrespondences
posing
output
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Computing Correspondences
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Computing Correspondences
•The Global Point Signature (GPS) frameworkby [Rustamov 2007]:
•Apply Gaussian-weighted Graph Laplacian onthe adjacency graph of input point cloud
GraphLaplacian
GPS in dimension 1, 2 and 3
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Computing Correspondences
•GPS is invariant under isometric deformation
•We choose only the first three eigenfunctions
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Computing Correspondences
•Correspondences areexpanded from 5extremums of alignedGPS embeddings
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Posing Template Mesh
•Formulated as an energy minimization problem:
• is the internal energy of template mesh
○ measures difference between template and thepoint cloud
•Solved by Invertible FEM [Irving et al. 2004]
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•It has two stages
Point cloud alignment:deformed by input point cloud
Posing Template Mesh
Initial alignment:
deformed by correspondence
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Posing Template Mesh
•For efficiency, posing isperformed on a simplifiedtemplate mesh, calledcontrol mesh
•Deformed template isrecovered by Mean ValueCoordinates [Ju et al.2005], i.e., weighted sum ofthe control mesh
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Results
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Results
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Results
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Results
(a) input scan (b) our method (c) SCAPE
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Results
• can be used tocontrol the body size
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Supplementary Video
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Discussion
•An automatic approach to align a detailedtemplate mesh with human point clouds indifferent poses
•Robust to noise and occlusions
•Sensitive to topological change
•Can not handle details like fingers
•Does not run in real-time
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Q & A
Thank you!