EXCOL: An EXtract-and-COmplete Layering Approach to Cartoon Animation Reusing

Huang, Hua; Fu, Hongbo

doi:10.1109/tvcg.2011.111

Cited by 19 publications

(7 citation statements)

References 36 publications

(70 reference statements)

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“…Similar to the MRF based frameworks, GAC with level set method has also not been used to handle the possibly asymmetrically distributed FP and FN. Besides, other video cutout frameworks have been proposed [14], [15], [16]. Our contribution is parallel to these directions towards accurate and user-friendly video cutout.…”

Section: Related Workmentioning

confidence: 99%

Segmentation Rectification for Video Cutout via One-Class Structured Learning

Wang,

Yeung,

Wang

et al. 2016

Preprint

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Recent works on interactive video object cutout mainly focus on designing dynamic foreground-background (FB) classifiers for segmentation propagation. However, the research on optimally removing errors from the FB classification is sparse, and the errors often accumulate rapidly, causing significant errors in the propagated frames. In this work, we take the initial steps to addressing this problem, and we call this new task segmentation rectification. Our key observation is that the possibly asymmetrically distributed false positive and false negative errors were handled equally in the conventional methods. We, alternatively, propose to optimally remove these two types of errors. To this effect, we propose a novel bilayer Markov Random Field (MRF) model for this new task. We also adopt the well-established structured learning framework to learn the optimal model from data. Additionally, we propose a novel one-class structured SVM (OSSVM) which greatly speeds up the structured learning process. Our method naturally extends to RGB-D videos as well. Comprehensive experiments on both RGB and RGB-D data demonstrate that our simple and effective method significantly outperforms the segmentation propagation methods adopted in the state-of-the-art video cutout systems, and the results also suggest the potential usefulness of our method in image cutout system.

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Section: Related Workmentioning

confidence: 99%

Segmentation Rectification for Video Cutout via One-Class Structured Learning

Wang,

Yeung,

Wang

et al. 2016

Preprint

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“…To this end, we have to estimate certain local geometry information around the strokes, even though we do not require physical correctness. This is a challenging problem as our input is only a single line drawing without temporal information (in contrast to problems tackled by Zhang et al [59] and Liu et al [55]), and the strokes are simply 2D polylines without labels and user annotations. To resolve these issues, we consider the following subproblems:…”

Section: Overviewmentioning

confidence: 99%

Globally Consistent Wrinkle-Aware Shading of Line Drawings

Jayaraman

Zheng

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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Shading is a tedious process for artists involved in 2D cartoon and manga production given the volume of contents that the artists have to prepare regularly over tight schedule. While we can automate shading production with the presence of geometry, it is impractical for artists to model the geometry for every single drawing. In this work, we aim to automate shading generation by analyzing the local shapes, connections, and spatial arrangement of wrinkle strokes in a clean line drawing. By this, artists can focus more on the design rather than the tedious manual editing work, and experiment with different shading effects under different conditions. To achieve this, we have made three key technical contributions. First, we model five perceptual cues by exploring relevant psychological principles to estimate the local depth profile around strokes. Second, we formulate stroke interpretation as a global optimization model that simultaneously balances different interpretations suggested by the perceptual cues and minimizes the interpretation discrepancy. Lastly, we develop a wrinkle-aware inflation method to generate a height field for the surface to support the shading region computation. In particular, we enable the generation of two commonly-used shading styles: 3D-like soft shading and manga-style flat shading.

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“…Other work [29] focuses on foreground segmentation for animation. In our case, in order to acquire real-time online matting for live video, it is inappropriate to include motion cues.…”

Section: Automatic Video Mattingmentioning

confidence: 99%

Practical automatic background substitution for live video

Huang

Fang

et al. 2017

Comp. Visual Media

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In this paper we present a novel automatic background substitution approach for live video. The objective of background substitution is to extract the foreground from the input video and then combine it with a new background. In this paper, we use a color line model to improve the Gaussian mixture model in the background cut method to obtain a binary foreground segmentation result that is less sensitive to brightness differences. Based on the high quality binary segmentation results, we can automatically create a reliable trimap for alpha matting to refine the segmentation boundary. To make the composition result more realistic, an automatic foreground color adjustment step is added to make the foreground look consistent with the new background. Compared to previous approaches, our method can produce higher quality binary segmentation results, and to the best of our knowledge, this is the first time such an automatic and integrated background substitution system has been proposed which can run in real time, which makes it practical for everyday applications.

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EXCOL: An EXtract-and-COmplete Layering Approach to Cartoon Animation Reusing

Cited by 19 publications

References 36 publications

Segmentation Rectification for Video Cutout via One-Class Structured Learning

Segmentation Rectification for Video Cutout via One-Class Structured Learning

Globally Consistent Wrinkle-Aware Shading of Line Drawings

Practical automatic background substitution for live video

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