Qingzheng Zheng
Sketching-Based Skeleton Generation
Zheng, Qingzheng; Li, Frederick Li; Lau, Rynson
Abstract
Articulated character animation can be performed by manually creating and rigging a skeleton into an unfolded 3D object. Such tasks are not trivial, as it requires a substantial amount of training and practices. Although methods have been proposed to help automatic extraction of skeleton structure, they may not guarantee that the resulting skeleton can help produce desired animations according to user intention. In this paper, we present a sketching-based skeleton generation method suitable for use in the mobile environment. This method takes user sketching as an input, and based on the mesh segmentation result of a 3D object, it estimates a skeleton for articulated character animation. Results show that our method can produce better skeletons in terms of joint positions and topological structure.
Citation
Zheng, Q., Li, F. L., & Lau, R. (2010). Sketching-Based Skeleton Generation. In 2010 3rd IEEE International Conference on Ubi-Media Computing (U-Media 2010), 5-6 July 2010, Jinhua, China (179-186). https://doi.org/10.1109/umedia.2010.5544472
Conference Name | 2010 3rd IEEE International Conference on Ubi-Media Computing (U-Media 2010) |
---|---|
Conference Location | Jinhua, China |
Online Publication Date | Aug 9, 2010 |
Publication Date | 2010 |
Deposit Date | Aug 31, 2010 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 179-186 |
Book Title | 2010 3rd IEEE International Conference on Ubi-Media Computing (U-Media 2010), 5-6 July 2010, Jinhua, China. |
DOI | https://doi.org/10.1109/umedia.2010.5544472 |
Keywords | skeleton generation, segmentation |
You might also like
IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation
(2023)
Journal Article
STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos
(2022)
Conference Proceeding
Distillation of human–object interaction contexts for action recognition
(2022)
Journal Article
STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising
(2021)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search