DC Field | Value | Language |
---|---|---|
dc.contributor.author | Liu, Minghua | ko |
dc.contributor.author | Sung, Minhyuk | ko |
dc.contributor.author | Mech, Radomir | ko |
dc.contributor.author | Su, Hao | ko |
dc.date.accessioned | 2021-11-08T06:43:51Z | - |
dc.date.available | 2021-11-08T06:43:51Z | - |
dc.date.created | 2021-11-03 | - |
dc.date.created | 2021-11-03 | - |
dc.date.created | 2021-11-03 | - |
dc.date.issued | 2021-06 | - |
dc.identifier.citation | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.12 - 21 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288937 | - |
dc.description.abstract | We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation. A new deformation can then be generated by sampling the co-efficients of the meta-handles in a specific range. We employ biharmonic coordinates as the deformation function, which can smoothly propagate the control points’ translations to the entire mesh. To avoid learning zero deformaion as meta-handles, we incorporate a target-fitting module which deforms the input mesh to match a random target. To enhance deformations’ plausibility, we employ a soft-rasterizer-based discriminator that projects the meshes to a 2D space. Our experiments demonstrate the superiority of the generated deformations as well as the interpretability and consistency of the learned meta-handles. The code is available at https://github.com/Colin97/DeepMetaHandles. | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.title | DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates | - |
dc.type | Conference | - |
dc.identifier.wosid | 000739917300002 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 12 | - |
dc.citation.endingpage | 21 | - |
dc.citation.publicationname | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Nashville, TN | - |
dc.identifier.doi | 10.1109/cvpr46437.2021.00008 | - |
dc.contributor.localauthor | Sung, Minhyuk | - |
dc.contributor.nonIdAuthor | Liu, Minghua | - |
dc.contributor.nonIdAuthor | Mech, Radomir | - |
dc.contributor.nonIdAuthor | Su, Hao | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.