Adversarial Colorization of Icons Based on Contour and Color Conditions

Sun, Tsai-Ho; Lai, Chien-Hsun; Wong, Sai‐Keung; Wang, Yu-Shuen

doi:10.1145/3343031.3351041

Cited by 48 publications

(34 citation statements)

References 33 publications

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“…Besides, we also investigated using reference-based colorization methods from other domains to our sketch colorization task. Icon colorization [45] and photo colorization [35] were tested. A visual comparison is presented in Fig.…”

Section: Qualitative Evaluationsmentioning

confidence: 99%

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Reference-guided structure-aware deep sketch colorization for cartoons

Liu

et al. 2021

Comp. Visual Media

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Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading. During colorization, the artist usually takes an existing cartoon image as color guidance, particularly when colorizing related characters or an animation sequence. Reference-guided colorization is more intuitive than colorization with other hints, such as color points or scribbles, or text-based hints. Unfortunately, reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading. In this paper, we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image. Our framework contains a color style extractor to extract the color feature from a color image, a colorization network to generate multi-scale output images by combining a sketch and a color feature, and a multi-scale discriminator to improve the reality of the output image. Extensive qualitative and quantitative evaluations show that our method outperforms existing methods, providing both superior visual quality and style reference consistency in the task of reference-based colorization.

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Section: Qualitative Evaluationsmentioning

confidence: 99%

“…We can observe that Ref. [45] learns to propagate the colors from the reference, but the output colors are too saturated for real-life sketch colorization purposes. Ref.…”

Section: Qualitative Evaluationsmentioning

confidence: 99%

Reference-guided structure-aware deep sketch colorization for cartoons

Liu

et al. 2021

Comp. Visual Media

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“…With the increasing quality of predictions, contour detectors are gradually appeared in multimedia applications [15,21,41]. For instance, [41] presents a novel icon generation method, which uses object contours as structure of condition to control the shape of the generated icon. We believe that in the future, there will be more applications relying on contour detection as long as the quality of prediction fulfills the demands.…”

Section: Related Workmentioning

confidence: 99%

Deep Structural Contour Detection

Deng

Liu

2020

Proceedings of the 28th ACM International Conference on Multimedia

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Object contour detection is the fundamental and preprocessing step for multimedia applications such as icon generation, object segmentation, and tracking. The quality of contour prediction is of great importance in these applications since it affects the subsequent process. In this work, we aim to develop a high-performance contour detection system. We first propose a novel yet very effective loss function for contour detection. The proposed loss function is capable of penalizing the distance of contour-structure similarity between each pair of prediction and ground-truth. Moreover, to better distinguishing object contours and background textures, we introduce a novel convolutional encoder-decoder network. Within the network, we present a hyper module that captures dense connections among high-level features and produces effective semantic information. Then the information is progressively propagated and fused with low-level features. We conduct extensive experiments on the BSDS500 and Multi-Cue datasets, the results show significant improvement against the state-of-the-art competitors. We further demonstrate the benefit of our DSCD method for crowd counting.

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“…It has been applied to support advanced tasks, such as [23], which uses color attributes to enhance object detection performance, and [24], which builds a color correction application to produce more optimal results. Most recently, in the comic industry, colorization methods have been actively developed to substantially reduce costs and labor [9], [25], [26], [27].…”

Section: Colorization Based On Deep Learningmentioning

confidence: 99%

Image Colorization Using the Global Scene-Context Style and Pixel-Wise Semantic Segmentation

Nguyen-Quynh

Kim

2020

IEEE Access

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In this paper, we present an encoder-decoder architecture that exploits global and local semantics for the automatic image colorization problem. For the global semantics, the low-level encoding features are fine-tuned by the scene-context classification to integrate the global image style. Moreover, the architecture deals with the uncertainty and relations among the scene styles based on the label smoothing and pre-trained weights from Places365. For local semantics, three branches learn the mutual benefits at the pixel-level, in which average and multi-modal distributions are respectively created from regression and soft-encoding branches, while the segmentation branch determines to which object the pixel belongs. Our experiments, which involve training with the Coco-Stuff dataset and validation on DIV2K, Places365, and ImageNet, show that our results are very encouraging.

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Adversarial Colorization of Icons Based on Contour and Color Conditions

Cited by 48 publications

References 33 publications

Reference-guided structure-aware deep sketch colorization for cartoons

Reference-guided structure-aware deep sketch colorization for cartoons

Deep Structural Contour Detection

Image Colorization Using the Global Scene-Context Style and Pixel-Wise Semantic Segmentation

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