發(fā)布時(shí)間：2018-04-29 12:35

  本文選題：動(dòng)畫生成 + 語(yǔ)義檢索��； 參考：《湖南大學(xué)》2012年碩士論文

【摘要】：動(dòng)畫生成技術(shù)是結(jié)合了人工智能理論與現(xiàn)代多媒體技術(shù)的全新動(dòng)畫制作過程。系統(tǒng)接收以自然語(yǔ)言描述的劇本,使用人工智能、語(yǔ)義檢索、空間推理與布局等相關(guān)技術(shù)對(duì)故事劇本進(jìn)行分析、理解,提取場(chǎng)景、事件、人物等信息,并在動(dòng)漫領(lǐng)域知識(shí)庫(kù)的協(xié)助下,采用語(yǔ)義標(biāo)注技術(shù)將多媒體信息(動(dòng)漫資源)語(yǔ)義化,并采用語(yǔ)義檢索引擎對(duì)相關(guān)劇本進(jìn)行語(yǔ)義檢索查詢和推理,最終實(shí)現(xiàn)動(dòng)畫的生成。 本文主要對(duì)動(dòng)漫領(lǐng)域語(yǔ)義檢索及場(chǎng)景生成關(guān)鍵技術(shù)進(jìn)行了研究,在論文的前半部分我們主要對(duì)動(dòng)漫領(lǐng)域語(yǔ)義檢索的關(guān)鍵技術(shù)進(jìn)行了研究,通過分析語(yǔ)義檢索中的一些關(guān)鍵技術(shù),提出了基于本體的查詢擴(kuò)展機(jī)制和加權(quán)排序優(yōu)化算法,并結(jié)合本體的具體特點(diǎn),提出了面向查詢的動(dòng)漫語(yǔ)義檢索框架,借助LarKC開發(fā)平臺(tái),采用LarKC插件集中的SPARQL識(shí)別器初步實(shí)現(xiàn)了一種面向查詢的動(dòng)漫語(yǔ)義檢索系統(tǒng)。本系統(tǒng)采用OWL作為本體的描述語(yǔ)言,基于動(dòng)漫制作領(lǐng)域建立相關(guān)本體,實(shí)現(xiàn)了動(dòng)漫領(lǐng)域知識(shí)的描述,建立了動(dòng)漫本體知識(shí)庫(kù)和規(guī)則庫(kù)；并通過語(yǔ)義標(biāo)注來(lái)進(jìn)行動(dòng)漫素材的語(yǔ)義化,并借助Jena開發(fā)工具,實(shí)現(xiàn)了針對(duì)動(dòng)漫領(lǐng)域知識(shí)的本體推理和查詢,并對(duì)系統(tǒng)的查詢效率、查全率與查準(zhǔn)率做了一系列的實(shí)測(cè)實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明：實(shí)驗(yàn)得到的動(dòng)漫素材能夠滿足檢索需求,同時(shí)也驗(yàn)證了動(dòng)漫素材領(lǐng)域本體,確實(shí)保證了系統(tǒng)的查準(zhǔn)率與查全率,驗(yàn)證了檢索框架的實(shí)用性與有效性。論文的后半部分我們主要對(duì)場(chǎng)景生成的相關(guān)方法進(jìn)行了研究,論文從定性和定量?jī)煞矫鎸?chǎng)景生成進(jìn)行了簡(jiǎn)要的討論,分析了場(chǎng)景生成的一些關(guān)鍵技術(shù)和方法,提出了場(chǎng)景生成算法和針對(duì)物體間關(guān)系用來(lái)計(jì)算物體位置和方向的場(chǎng)景物體空間關(guān)系布局推理方法等。最后,我們提出了場(chǎng)景生成系統(tǒng)的框架和設(shè)計(jì)思想,對(duì)于某一段故事劇本,動(dòng)漫本體描述語(yǔ)言能將其以RDF/N3形式進(jìn)行描述,也就是說(shuō),我們能以RDF三元組的形式來(lái)描述某一段劇本,或者說(shuō)可以把某一段劇本以RDF文件的形式來(lái)表達(dá)。然后通過SPARQL語(yǔ)言查詢匹配動(dòng)漫本體描述語(yǔ)言的事件(Action),再根據(jù)Action各故事劃分成場(chǎng)景,然后根據(jù)每個(gè)場(chǎng)景的背景描述查找場(chǎng)景庫(kù),生成該背景下的典型場(chǎng)景,之后對(duì)動(dòng)漫本體描述語(yǔ)言中空間信息進(jìn)行分析,然后加上物體規(guī)則庫(kù)動(dòng)態(tài)修改場(chǎng)景信息,最后添加該場(chǎng)景中出現(xiàn)的人物信息,得到最終的場(chǎng)景。
[Abstract]:Animation generation technology is a new animation production process which combines artificial intelligence theory and modern multimedia technology. The system receives scripts described in natural language, uses artificial intelligence, semantic retrieval, spatial reasoning and layout techniques to analyze, understand, extract scenes, events, characters, etc. With the help of animation domain knowledge base, semantic annotation technology is used to make multimedia information (animation resource) semantic, and semantic retrieval engine is used to query and infer the relevant scripts. Finally, animation generation is realized. In the first half of this paper, we mainly study the key technologies of semantic retrieval in animation field, and analyze some key technologies in semantic retrieval. A query extension mechanism based on ontology and a weighted sorting optimization algorithm are proposed. Combined with the specific characteristics of ontology, a query oriented animation semantic retrieval framework is proposed, which is based on LarKC development platform. A query oriented animation semantic retrieval system is implemented using SPARQL recognizer in the LarKC plug-in set. This system uses OWL as the description language of ontology, establishes the related ontology based on the animation production domain, realizes the description of the knowledge in the animation field, and establishes the knowledge base and rule base of the animation ontology. With the help of Jena, the ontology reasoning and query of animation domain knowledge are realized, and a series of experiments are made on the query efficiency, recall and precision of the system. The experimental results show that the animation materials obtained from the experiment can meet the retrieval requirements, and also verify the ontology of animation material domain, which ensure the precision and recall of the system, and verify the practicability and effectiveness of the retrieval framework. In the second half of the thesis, we mainly study the related methods of scene generation, discuss the scene generation from qualitative and quantitative aspects, and analyze some key techniques and methods of scene generation. A scene generation algorithm and a reasoning method for spatial relation layout of scene objects are proposed, which are used to calculate the position and direction of objects. Finally, we put forward the framework and design idea of scene generation system. For a story script, animation ontology description language can describe it in the form of RDF/N3, that is, We can describe a script in the form of RDF triples, or we can express a script as a RDF file. Then the events matching animation ontology description language are queried by SPARQL language, and then divided into scenes according to each Action story. Then, the scene library is searched according to the background description of each scene, and the typical scene under this background is generated. Then it analyzes the spatial information in the animation ontology description language, then dynamically modifies the scene information by adding the object rule base, and finally adds the character information in the scene to get the final scene.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.3

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