Sulistyono, Arif and Atmani, A. K. Pritha and Gunanto, S. Gandang and Troy, Troy (2017) Toward ti exaggeration engine for facial animation: evaluating the diference of RBF implementation in expression-marker transfer. In: Proceedings of 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS 2017), 08 November 207, Yogyakarta.
Full text not available from this repository.Abstract
The human face has a unique shape and size, as well as a 3D character face model. During this process of animated facial expression of 3D virtual characters are mostly still done manually by moving the rig in each frame. The more characters used, the more production costs incurred. The absence of a cheap facial motion transfer system is also one of the reasons why not many studios are using motion capture technology in Indonesia. This study will evaluate the implementation of radial basis function (RBF) as a method of marker-transfer used as a reference to rig movement in the facial animation system. Testing is done by performing variations of radial function, namely: Gaussian, Inverse Quadratic, Inverse Multiquadric, and Multiquadric. The value of epsilon used is 0.01. The experimental results show that the range of feature point shifts generated by RBF Gaussian, Inverse Multiquadric, Inverse Quadratic, and Multiquadric have the same pattern with the difference in the distance of the marker rigging point shift on the target 3D model. The farthest range of differences is generated by RBF Gaussian. The resulting maximum range difference can reach 92.37% and a minimum of 2.47% compared to other methods. This appears on the Gaussian chart to have a sharper pattern. As with the concept of exaggeration, the Gaussian method which has the maximum range is the right method to apply the exaggeration principle in 3D faceexpression animation.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods > 006.3 Artificial Intelligence, Machine Learning, Pattern Recognition, Data Mining |
| Divisions: | Universitas Multimedia Nusantara |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 23 Feb 2018 08:33 |
| Last Modified: | 21 Jun 2023 01:54 |
| URI: | https://kc.umn.ac.id/id/eprint/2772 |
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