Gunanto, Samuel Gandang (2018) Sintesis Ekspresi Wajah Realistik Berbasis Feature-Point Cluster Menggunakan Radial Basis Function. Doctoral thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Meningkatnya permintaan produk animasi oleh rumah produksi dan
stasiun televisi menuntut adanya perubahan yang signifikan di dalam proses
produksi animasi. Penelitian animasi ekspresi pada wajah khususnya mengenai
proses rigging dan pemindahan ekspresi semakin banyak. Pendekatan tradisional
animasi ekspresi wajah sangat tergantung pada animator dalam pembuatan gerakan
kunci dan rangkaian gerakan ekspresi wajah. Hal ini menyebabkan produksi
animasi wajah untuk satu wajah tidak dapat digunakan ulang secara langsung untuk
wajah lainnya karena kekhususannya tersebut. Oleh karena itu proses otomatisasi
pembentukan area pembobotan pada model wajah 3D dengan pendekatan cluster
berikut proses duplikasi gerak yang adaptif terhadap bentuk wajah untuk
mempersingkat proses produksi animasi sangat penting.
Prinsip animasi dipandang sebagai salah satu solusi dan panduan untuk
pembuatan animasi gerak wajah yang ekspresif dan hidup. Sintesis ekspresi wajah
realistik dapat dibuat dengan basis feature-point cluster menggunakan radial basis
function. Otomatisasi pembentukan area gerak di wajah hasil proses clustering
berdasarkan letak fitur titik dan proses retargeting menggunakan radial basis
function untuk melakukan sintesis ekspresi wajah realistik merupakan kebaruan
yang diangkat pada penelitian ini.
Berdasarkan semua tahapan eksperimentasi yang dilakukan dapat
disimpulkan bahwa sintesis ekspresi wajah realistik dengan basis feature-point
cluster menggunakan radial basis function dapat diterapkan pada beragam model
wajah 3D dan dapat secara adaptif peka terhadap bentuk wajah dari masing-masing
model 3D yang memiliki jumlah fitur penanda yang sama. Hasil persepsi visual
evaluasi penerapan sintesis ekspresi wajah realistik menunjukkan hasil ekspresi
terkejut memiliki persentasi paling tinggi mudah dikenali, yaitu: 89,32%. Ekspresi
senang: 84,63 %, ekspresi sedih: 77,32%, ekspresi marah: 76,64%, ekspresi jijik:
76,45%, serta ekspresi takut: 76,44%. Rerata persentase wajah mudah dikenali
sebesar 80,13%.
================================================================================================================== The increasing demand of animated movies by production houses and
television stations needs a significant change in the animation production process.
Computer facial animation research on the process of rigging and expression
transfer is growing. The traditional approach of facial animation is highly
dependent on the animator in making the key and the sequence of facial expression
movements. This causes the production of facial animation for one face can not be
reused directly for the other face because of its uniqueness. Therefore, the process
of automating the formation of weighted areas on 3D face model with cluster
approach and adaptive motion transfer process to face shape is very important to
shorten the production process of animation.
The principle of animation is seen as one of the solutions and guidelines
for the creation of animated facial expression expressively. The synthesis of
realistic facial expression can be made on the basis of a feature-point cluster using
a radial basis function. Automation process for formatting the motion area in the
face by clustering process based on the location of the feature-point and retargeting
process using radial basis function to perform synthesis of realistic facial expression
is the novelty of this research.
Based on all experimentation stages, it can be concluded that the synthesis
of realistic facial expression based on a feature-point cluster using radial basis
function can be applied to various 3D face models and can be adaptively sensitive
to the facial shape of each 3D model which has the same number of marker features.
The results of visual perception evaluation from the synthesis of realistic facial
expression show that surprise expression has the highest percentage and easily
recognizable, 89,32%. Happy expression: 84,63%, sad expression: 77,32%, angry
expression: 76,64%, disgust expression: 76,45%, and a fear expression: 76,44%.
The average percentage of faces is easily recognizable at 80,13%.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Additional Information: | RDE 621.367 Gun s |
| Uncontrolled Keywords: | Sintesis ekspresi; pendekatan cluster; radial basis function; Expression Synthesis; cluster approach |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques T Technology > TR Photography > TR897.7 Computer animation |
| Divisions: | Faculty of Electrical Technology > Electrical Engineering > 20001-(S3) PhD Thesis |
| Depositing User: | Gunanto Samuel Gandang |
| Date Deposited: | 03 May 2018 04:01 |
| Last Modified: | 24 Jun 2020 08:04 |
| URI: | http://repository.its.ac.id/id/eprint/51419 |
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