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<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName>Tarbiat Modares University</PublisherName>
				<JournalTitle>The Modares Journal of Electrical Engineering</JournalTitle>
				<Issn>2228-527X</Issn>
				<Volume>10</Volume>
				<Issue>4</Issue>
				<PubDate PubStatus="epublish">
					<Year>2011</Year>
					<Month>01</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Merging Key-Framing and Performance-Driven Facial Animation</ArticleTitle>
<VernacularTitle>ترکیب قاب بندی کلیدی و پویانمایی چهره مبتنی بر نمایش</VernacularTitle>
			<FirstPage>37</FirstPage>
			<LastPage>53</LastPage>
			<ELocationID EIdType="pii">12808</ELocationID>
			
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Hoda</FirstName>
					<LastName>Bahonar</LastName>
<Affiliation>Tarbiat Modares University</Affiliation>

</Author>
<Author>
					<FirstName>Nasrallah</FirstName>
					<LastName>Moghadam Charkari</LastName>
<Affiliation>Tarbiat Modares University</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
		<Abstract>The goal of facial animation is to synthesize realistic facial animated images using computer graphics. Because of its capability in creating facial animated images using a few amount of information, facial animation using feature vectors was extensively studied in recent years. In general, this method is considered as one of the performance-driven facial animation base methods. In this regard, facial feature vectors, which reflect rigid and non-rigid movements of the face, are extracted using facial analysis methods. These feature vectors are then used for transferring the facial movements to a graphical model. Our approach in this paper is merging keyframing and facial animation using feature vectors. Using this method, the amount of the submitted information is decreased to about 30%; while the synthesized sequences have 4.95% mean squared error and 0.000629 difference of correlation relative to input sequences. This error is negligible compared to the error of synthesized sequences without using interpolation.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Performance-Driven Facial Animation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Key framing</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Face Reconstruction</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Telecommunication</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Virtual Learning</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://mjee.modares.ac.ir/article_12808_2959ced850c7261ff93292042fed7240.pdf</ArchiveCopySource>
</Article>
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