Pencil drawing animation from a video

Liang, Dongxue; Park, Kyoungju

doi:10.1002/cav.1520

Cited by 3 publications

(4 citation statements)

References 35 publications

(103 reference statements)

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“…Seo and Ostromoukhov applied a particle video method to produce pointillist video stylizations [16]. Liang and Park combined stroke models and rigid-body dynamics; thus, their strokes translated and rotated due to optical flows from corresponding pixels [17].…”

Section: Automatic Video Paintingmentioning

confidence: 99%

“…Point-based rendering techniques, working on a per-pixel basis to achieve stylization as either abstractions or cartoons, translate the rendered particles and produce video stylization [13][14][15][16][17][18][19]. Unlike point-based rendering techniques, painterly rendering uses strokes as the basic units and considers both the translation and rotation of strokes because of their spatial extensions.…”

Section: Introductionmentioning

confidence: 99%

“…Among numerous video painterly rendering techniques [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], the major scheme uses a set of image processing methods that includes blur, edge detection, tangent flow, segmentation, feature extraction, saliency maps, and optical flows. Since most image processing methods are sensitive to illumination variations and noise in the source images, the quality of the resulting outputs depends on the quality of the image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

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Deformable strokes towards temporally coherent video painting

et al. 2016

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We present an automatic and robust technique for creating non-photorealistic rendering (NPR) and animation, starting from a video that depicts the shape details and follows the motion of underlying objects. We generate NPR from the initial frame of the source video using a greedy algorithm for stroke placements and models, in combination with a saliency map and a flow-guided difference-of-Gaussian filter. Our stroke model uses a set of triangles whose vertices are particles and whose edges are springs. Using a physicsbased framework, the generated and rendered strokes are translated, rotated and deformed by forces exerted from the sequential frames. External forces acting on strokes are calculated according to temporally and spatially smoothed per-pixel optical flow vectors. After simulating each frame, we delete unnecessary strokes and add new strokes for disappearing and appearing objects, but only if necessary to avoid popping and scintillation. Our framework automatically generates the coherent animation of rendered strokes, preserving the appearance details and animating strokes along with the underlying objects. This had been difficult to achieve with previous user-guided methods and automatic but limited transformations methods.

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Section: Automatic Video Paintingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deformable strokes towards temporally coherent video painting

et al. 2016

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“…In the process of achieving this goal, it is very important to avoid the effect of the source image's illumination and noise. Recently, researchers have paid more and more attention to two main kinds of techniques: the point-based rendering method [14][15][16][17][18][19][20], which works on a per-pixel basis to obtain stylized video; the stroke-based rendering method [7][8][9][10], which uses stroke as a basic rendering unit to achieve the rendering results. However, the point-based methods have no stroke models and the stroke-based methods lack the deformation of strokes, so their animation results tend to be insensitive to the motion of the object, which is important for following the underlying motion coherently.…”

Section: Introductionmentioning

confidence: 99%

Facial Feature Model for a Portrait Video Stylization

Liang¹,

Park²,

Krompiec³

2018

Symmetry

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With the advent of the deep learning method, portrait video stylization has become more popular. In this paper, we present a robust method for automatically stylizing portrait videos that contain small human faces. By extending the Mask Regions with Convolutional Neural Network features (R-CNN) with a CNN branch which detects the contour landmarks of the face, we divided the input frame into three regions: the region of facial features, the region of the inner face surrounded by 36 face contour landmarks, and the region of the outer face. Besides keeping the facial features region as it is, we used two different stroke models to render the other two regions. During the non-photorealistic rendering (NPR) of the animation video, we combined the deformable strokes and optical flow estimation between adjacent frames to follow the underlying motion coherently. The experimental results demonstrated that our method could not only effectively reserve the small and distinct facial features, but also follow the underlying motion coherently.

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Auxiliary Design of Oil Painting by Computer Technology Under the Background of Big Data

Jia

2021

Application of Big Data, Blockchain, and Internet of Things for Education Informatization

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Pencil drawing animation from a video

Cited by 3 publications

References 35 publications

Deformable strokes towards temporally coherent video painting

Deformable strokes towards temporally coherent video painting

Facial Feature Model for a Portrait Video Stylization

Auxiliary Design of Oil Painting by Computer Technology Under the Background of Big Data

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