OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation

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dc.contributor.authorKim, Kunhoen_US
dc.contributor.authorUy, Mikaela Angelinaen_US
dc.contributor.authorPaschalidou, Despoinaen_US
dc.contributor.authorJacobson, Alecen_US
dc.contributor.authorGuibas, Leonidas J.en_US
dc.contributor.authorSung, Minhyuken_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:35:51Z
dc.date.available2023-10-09T07:35:51Z
dc.date.issued2023
dc.description.abstractWe propose OPTCTRLPOINTS, a data-driven framework designed to identify the optimal sparse set of control points for reproducing target shapes using biharmonic 3D shape deformation. Control-point-based 3D deformation methods are widely utilized for interactive shape editing, and their usability is enhanced when the control points are sparse yet strategically distributed across the shape. With this objective in mind, we introduce a data-driven approach that can determine the most suitable set of control points, assuming that we have a given set of possible shape variations. The challenges associated with this task primarily stem from the computationally demanding nature of the problem. Two main factors contribute to this complexity: solving a large linear system for the biharmonic weight computation and addressing the combinatorial problem of finding the optimal subset of mesh vertices. To overcome these challenges, we propose a reformulation of the biharmonic computation that reduces the matrix size, making it dependent on the number of control points rather than the number of vertices. Additionally, we present an efficient search algorithm that significantly reduces the time complexity while still delivering a nearly optimal solution. Experiments on SMPL, SMAL, and DeformingThings4D datasets demonstrate the efficacy of our method. Our control points achieve better template-to-target fit than FPS, random search, and neural-network-based prediction. We also highlight the significant reduction in computation time from days to approximately 3 minutes.en_US
dc.description.number7
dc.description.sectionheadersMotion Capture and Generation
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14963
dc.identifier.issn1467-8659
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14963
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14963
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Mesh models; Mesh geometry models; Shape analysis
dc.subjectComputing methodologies
dc.subjectMesh models
dc.subjectMesh geometry models
dc.subjectShape analysis
dc.titleOptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformationen_US
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