本文關(guān)鍵詞：針對視頻的人臉卡通化方法研究　出處：《電子科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 人臉卡通化 回歸樹模型 人臉夸張化 薄樣條函數(shù)

【摘要】：近幾年來,動漫產(chǎn)業(yè)呈現(xiàn)爆炸式的增長,卡通人物形象更是被創(chuàng)造性地應(yīng)用在視頻通話、角色游戲等方面,這些都離不開計算機輔助動畫合成技術(shù)在降低工作量、提高表現(xiàn)效果方面發(fā)揮著的關(guān)鍵性作用。其中,視頻人臉卡通化一方面要考慮視頻的實時性要求;另一方面,人臉表情變化豐富細微,要兼顧生成卡通與真實人臉的相似性,因此人臉卡通化一直是計算機輔助動畫合成技術(shù)面臨的重點和難點。現(xiàn)有的視頻卡通化方法需要復(fù)雜的預(yù)處理或大量的后期工作,難以實時地生成個性化的卡通人臉。本文圍繞視頻人臉卡通化這個問題進行了深入研究,在此基礎(chǔ)上,進一步研究了基于平均臉的人臉夸張化方法,具體研究內(nèi)容如下:1.提出了改進的交互式人臉卡通化方法,該方法能夠?qū)崿F(xiàn)在復(fù)雜背景下合成效果較好的卡通人臉,僅需要簡單的用戶交互。本文首先提出了基于回歸樹特征點模型的人臉卡通合成方法,針對人臉卡通輪廓線合成效果不好的問題,提出基于二次B樣條插值的修正方法;針對復(fù)雜背景下頭發(fā)區(qū)域分割效果不好的問題,提出了分塊閉操作方法。2.提出了一種人臉視頻卡通生成方法,包括基于器官狀態(tài)的相似幀查找以及基于回歸樹特征點模型的卡通驅(qū)動。針對基于灰度值的相似幀查找錯誤率高的問題,提出以器官的狀態(tài)作為兩幀相似的判定標準,改善了查找的準確性�；诨貧w樹模型的卡通驅(qū)動將視頻中器官的運動分解為剛性運動和柔性運動,通過對視頻的壓縮處理提高特征點的檢測速度,并根據(jù)檢測到的器官運動參數(shù)驅(qū)動相應(yīng)的卡通器官完成對兩種運動的響應(yīng)。3.提出了基于平均臉的兩種人臉夸張化方法:人臉整體夸張方法和針對突出器官的局部夸張方法。前者分別構(gòu)造并比較測試人臉與平均人臉之間的特征向量,對所有存在差異的部分使用薄樣條插值函數(shù)進行夸張變形;后者根據(jù)歐氏距離得到最突出的器官,并對測試人臉的臉型進行判定,最后僅對最突出器官及臉型進行夸張變形。4.視頻人臉卡通化系統(tǒng)開發(fā)。該系統(tǒng)能夠通過簡單的人機交互實現(xiàn)視頻人臉卡通化算法以及對人臉的夸張化算法。
[Abstract]:In recent years, the animation industry shows an explosive growth, cartoon characters are creatively used in video calls, character games and so on. These are all inseparable from the computer aided animation synthesis technology in reducing the workload and improving the performance of the key role played by the video face card on the one hand to consider the real-time requirements of video; On the other hand, the facial expression is rich and subtle, so the similarity between cartoon generation and real face should be taken into account. Therefore, face card generalization has always been the focus and difficulty of computer-aided animation synthesis technology. The existing video card generalization methods need complex preprocessing or a lot of later work. It is difficult to generate personalized cartoon face in real time. This paper focuses on the problem of video face card generalization. On this basis, the method of facial exaggeration based on average face is further studied. The specific research contents are as follows: 1. An improved interactive face card generalization method is proposed, which can synthesize cartoon face with better effect in complex background. Only simple user interaction is required. Firstly, a face cartoon synthesis method based on regression tree feature point model is proposed. A modified method based on quadratic B-spline interpolation is proposed. In order to solve the problem that the segmentation effect of hair region in complex background is not good, a block closed operation method is proposed. 2. A face video cartoon generation method is proposed. It includes similar frame lookup based on organ state and cartoon driver based on regression tree feature point model. Aiming at the problem of high error rate of similar frame lookup based on gray value. Using the state of organs as the criterion of similarity between two frames, the accuracy of searching is improved. The motion of organs in video is decomposed into rigid motion and flexible motion by cartoon driver based on regression tree model. Video compression improves the detection speed of feature points. According to the detected organ motion parameters, the corresponding cartoon organs are driven to complete the response to the two movements. 3. Two face exaggeration methods based on average face are proposed. The whole exaggeration method of face is constructed and compared with the local exaggeration method for salient organs. The feature vectors between the tested face and the average face are constructed and compared respectively. For all the different parts, the thin spline interpolation function is used for hyperbolic deformation. The latter gets the most prominent organs according to the Euclidean distance and determines the face shape of the tested face. Finally, only the most prominent organs and facial patterns are exaggerated. 4. The video face card system is developed. The system can realize video face card generalization algorithm and face exaggeration algorithm through simple human-computer interaction.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

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相關(guān)期刊論文 前1條

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本文編號：1359375