Manga, a popular form of multimodal artwork, has traditionally been overlooked in deep learning advancements due to the absence of a robust dataset and comprehensive annotation. Manga segmentation is the key to the digital migration of manga. There exists a significant domain gap between the manga and the natural images, that fails most existing learning-based methods. To address this gap, we introduce an augmented segmentation annotation for the Manga109 dataset, a collection of 109 manga volumes, that offers intricate artworks in a rich variety of styles. We introduce a detailed annotation that extends beyond the original simple bounding boxes to the segmentation masks with pixel-level precision. It provides object category, location, and instance information that can be used for semantic segmentation and instance segmentation. We also provide a comprehensive analysis of our annotation dataset from various aspects. We further measure the improvement of the state-of-the-art segmentation model after training it with our augmented dataset. The benefits of this augmented dataset are profound, with the potential to significantly enhance manga analysis algorithms and catalyze the novel development in digital art processing and cultural analytics. This annotation, named MangaSeg, is publicly available at https://huggingface.co/datasets/MS92/MangaSegmentation.

Stroke segmentation is a fundamental technique for various character analysis and synthesis applications. However, existing methods often face challenges such as over-segmentation, under-segmentation, low segmentation accuracy, and limited generalization capability when segmenting characters of diverse fonts. To address these issues, we propose a novel stroke segmentation method based on contour matching and utilize a similarity-based chain propagation strategy to tackle the challenges posed by fonts with significant structural and stylistic variations. Extensive visual and quantitative experiments on a newly created high-quality dataset demonstrate that our approach outperforms state-of-the-art methods and effectively handles a wide range of fonts.

Abstract Text‐guided video‐to‐video stylization transforms the visual appearance of a source video to a different appearance guided on textual prompts. Existing text‐guided image diffusion models can be extended for stylized video synthesis. However, they struggle to generate videos with both highly detailed appearance and temporal consistency. In this paper, we propose a synchronized multi‐frame diffusion framework to maintain both the visual details and the temporal consistency. Frames are denoised in a synchronous fashion, and more importantly, information of different frames is shared since the beginning of the denoising process. Such information sharing ensures that a consensus, in terms of the overall structure and color distribution, among frames can be reached in the early stage of the denoising process before it is too late. The optical flow from the original video serves as the connection, and hence the venue for information sharing, among frames. We demonstrate the effectiveness of our method in generating high‐quality and diverse results in extensive experiments. Our method shows superior qualitative and quantitative results compared to state‐of‐the‐art video editing methods.

Abstract As a popular comic style, manga offers a unique impression by utilizing a rich set ofbitonal patterns, or screentones, for illustration. However, screentones can easily be degraded when manga is resized in terms of aspect ratio and resolution for manga re‐layout and e‐manga migration applications. To tackle this problem, we propose the first automatic manga retargeting method that synthesizes a retargeted manga image while preserving the prominent structure and fine screentone intended by the manga artist. While modern natural photo retargeting methods can achieve prominent structure preservation, preserving screentones within arbitrarily shaped regions is very challenging due to two properties of manga: (i) pattern constancy under translation, and (ii) non‐compatibility with interpolation. To circumvent this barrier, we propose learning a quantized representation of screentones that is translation‐invariant and pointwisely representable through a tailored manga reconstruction network with a screentone‐anchored codebook. Thanks to these merits, we can perform the re‐synthesis operation using existing photo retargeting methods and achieve the desired manga retargeting results. We conducted extensive qualitative and quantitative experiments to validate the effectiveness of our method, and we achieved notably compelling results compared to alternative methods.

Cartoon animation video is a popular visual entertainment form worldwide, however many classic animations were produced in a 4:3 aspect ratio that is incompatible with modern widescreen displays. Existing methods like cropping lead to information loss while retargeting causes distortion. Animation companies still rely on manual labor to renovate classic cartoon animations, which is tedious and labor-intensive, but can yield higher-quality videos. Conventional extrapolation or inpainting methods tailored for natural videos struggle with cartoon animations due to the lack of textures in anime, which affects the motion estimation of the objects. In this article, we propose a novel framework designed to automatically outpaint 4:3 anime to 16:9 via region-guided motion inference. Our core concept is to identify the motion correspondences between frames within a sequence in order to reconstruct missing pixels. Initially, we estimate optical flow guided by region information to address challenges posed by exaggerated movements and solid-color regions in cartoon animations. Subsequently, frames are stitched to produce a pre-filled guide frame, offering structural clues for the extension of optical flow maps. Finally, a voting and fusion scheme utilizes learned fusion weights to blend the aligned neighboring reference frames, resulting in the final outpainting frame. Extensive experiments confirm the superiority of our approach over existing methods.

While manga is a popular entertainment form, creating manga is tedious, especially adding screentones to the created sketch, namely manga screening. Unfortunately, there is no existing method that tailors for automatic manga screening, probably due to the difficulty in generating shaded high-frequency screentones of high-quality. Classic manga screening approaches generally require user input to provide screentone exemplars or a reference manga image. Recent deep learning models enable automatic generation by learning from a large-scale dataset. However, the state-of-the-art models still fail to generate high-quality shaded screentones due to the lack of a tailored model and high-quality manga training data. In this paper, we propose a novel sketch-to-manga framework that first generates a color illustration from the sketch and then generates a screentoned manga based on the intensity guidance. Our method significantly outperforms existing methods in generating high-quality manga with shaded high-frequency screentones.

Shading plays an important role in cartoon drawings to present the 3D lighting and depth information in a 2D image to improve the visual information and pleasantness. But it also introduces apparent challenges in analyzing and processing the cartoon drawings for different computer graphics and vision applications, such as segmentation, depth estimation, and relighting. Extensive research has been made in removing or separating the shading information to facilitate these applications. Unfortunately, the existing researches only focused on natural images, which are natively different from cartoons since the shading in natural images is physically correct and can be modeled based on physical priors. However, shading in cartoons is manually created by artists, which may be imprecise, abstract, and stylized. This makes it extremely difficult to model the shading in cartoon drawings. Without modeling the shading prior, in the paper, we propose a learning-based solution to separate the shading from the original colors using a two-branch system consisting of two subnetworks. To the best of our knowledge, our method is the first attempt in separating shading information from cartoon drawings. Our method significantly outperforms the methods tailored for natural images. Extensive evaluations have been performed with convincing results in all cases.

Manga screening is a critical process in manga production, which still requires intensive labor and cost. Existing manga screening methods either generate simple dotted screentones only or rely on color information and manual hints during screentone selection. Due to the large domain gap between line drawings and screened manga, and the difficulties in generating high-quality, properly selected and shaded screentones, even state-of-the-art deep learning methods cannot convert line drawings to screened manga well. Besides, ambiguity exists in the screening process since different artists may screen differently for the same line drawing. In this article, we propose to introduce shaded line drawing as the intermediate counterpart of the screened manga so that the manga screening task can be decomposed into two sub-tasks, generating shading from a line drawing and replacing shading with proper screentones. The reference image is adopted to resolve the ambiguity issue and provides options and controls on the generated screened manga. We proposed a reference-based shading generation network and a reference-based screentone generation module to achieve the two sub-tasks individually. We conduct extensive visual and quantitative experiments to verify the effectiveness of our system. Results and statistics show that our method outperforms existing methods on the manga screening task.

Converting a human portrait to anime style is a desirable but challenging problem. Existing methods fail to resolve this problem due to the large inherent gap between two domains that cannot be overcome by a simple direct mapping. For this reason, these methods struggle to preserve the appearance features in the original photo. In this article, we discover an intermediate domain, the coser portrait (portraits of humans costuming as anime characters), that helps bridge this gap. It alleviates the learning ambiguity and loosens the mapping difficulty in a progressive manner. Specifically, we start from learning the mapping between coser and anime portraits, and present a proxy-guided domain adaptation learning scheme with three progressive adaptation stages to shift the initial model to the human portrait domain. In this way, our model can generate visually pleasant anime portraits with well-preserved appearances given the human portrait. Our model adopts a disentangled design by breaking down the translation problem into two specific subtasks of face deformation and portrait stylization. This further elevates the generation quality. Extensive experimental results show that our model can achieve visually compelling translation with better appearance preservation and perform favorably against the existing methods both qualitatively and quantitatively. Our code and datasets are available at https://github.com/NeverGiveU/PDA-Translation.

Network management tasks rely on precise and fine-grained network information to make correct and appropriate decisions. These tasks (e.g., DDoS detection) require network information with multiple flow definitions to better manage the network. However, the existing works mainly focus on the query of multiple flow definitions on a single switch, without a thoughtful solution for this query in network-wide measurement. In this paper, to address this problem, we overcome several challenges and propose MINT, a system that enables the query for multiple flow definitions in network-wide measurement. The key insights of MINT are: deploying MFSketch to measure multiple flow definitions information on the switch, cutting MFSketch into fixed-size slices, and using in-band telemetry (INT) to carry the slice to the analyzer. Therefore, after the analyzer collects and reorganizes the slices, network operators can query multiple flow definitions information of the whole network for various network management tasks. We implemented a prototype of MINT on a Barefoot Tofino switch. Experimental results show that MINT provides reliable transmission and consistency guarantees while only using switch resources comparable to state-of-the-art works, with less than 1% additional network overhead. Additionally, MFSketch provides accurate measurements for multiple flow definitions query, outperforming other solutions in both accuracy and F1 score.

Using a sequence of discrete still images to tell a story or introduce a process has become a tradition in the field of digital visual media. With the surge in these media and the requirements in downstream tasks, acquiring their main topics or genres in a very short time is urgently needed. As a representative form of the media, comic enjoys a huge boom as it has gone digital. However, different from natural images, comic images are divided by panels, and the images are not visually consistent from page to page. Therefore, existing works tailored for natural images perform poorly in analyzing comics. Considering the identification of comic genres is tied to the overall story plotting, a long-term understanding that makes full use of the semantic interactions between multi-level comic fragments needs to be fully exploited. In this paper, we propose [Formula: see text]Comic, a Panel-Page-aware Comic genre classification model, which takes page sequences of comics as the input and produces class-wise probabilities. [Formula: see text]Comic utilizes detected panel boxes to extract panel representations and deploys self-attention to construct panel-page understanding, assisted with interdependent classifiers to model label correlation. We develop the first comic dataset for the task of comic genre classification with multi-genre labels. Our approach is proved by experiments to outperform state-of-the-art methods on related tasks. We also validate the extensibility of our network to perform in the multi-modal scenario. Finally, we show the practicability of our approach by giving effective genre prediction results for whole comic books.

Vector graphics is broadly used in a variety of forms, such as illustrations, logos, posters, billboards, and printed ads. Despite its broad use, many artists still prefer to draw with pen and paper, which leads to a high demand of converting raster designs into the vector form. In particular, line drawing is a primary art and attracts many research efforts in automatically converting raster line drawings to vector form. However, the existing methods generally adopt a two-step approach, stroke segmentation and vectorization. Without vector guidance, the raster-based stroke segmentation frequently obtains unsatisfying segmentation results, such as over-grouped strokes and broken strokes. In this paper, we make an attempt in proposing an end-to-end vectorization method which directly generates vectorized stroke primitives from raster line drawing in one step. We propose a Transformer-based framework to perform stroke tracing like human does in an automatic stroke-by-stroke way with a novel stroke feature representation and multi-modal supervision to achieve vectorization with high quality and fidelity. Qualitative and quantitative evaluations show that our method achieves state of the art performance.

Dashed curve is a frequently used curve form and is widely used in various drawing and illustration applications. While humans can intuitively recognize dashed curves from disjoint curve segments based on the law of continuity in Gestalt psychology, it is extremely difficult for computers to model the Gestalt law of continuity and recognize the dashed curves since high-level semantic understanding is needed for this task. The various appear-ances and styles of the dashed curves posed on a potentially noisy background further complicate the task. In this paper, we propose an innovative Transformer-based framework to recognize dashed curves based on both high-level features and low-level clues. The framework manages to learn the computational analogy of the Gestalt Law in various do-mains to locate and extract instances of dashed curves in both raster and vector representations. Qualitative and quantitative evaluations demonstrate the efficiency and ro-bustness of our framework over all existing solutions.

The offshore wind power is an effective replacement for thermal power plants to realize the high renewable penetrated power system and guarantee the zero emissions. AC transmission and grid connection has the advantage among various solutions from the viewpoint of economics in implementation, where AC submarine cable is the medium of power transmission to deliver the offshore wind farm to the onshore grid. However, such offshore wind power system is more susceptible to the risk of high-frequency oscillation due to the highly distributed capacitance of AC submarine cables. To address this problem, this paper builds the impedance model for offshore wind power via ac submarine cable and further conducts the frequency-domain analysis towards the associated oscillation problem. Firstly, the impedance model of the collection network is constructed and validated. Furthermore, the system stability is analyzed based on the Nyquist criteria so as to analyze the effects of cable parameters on the oscillation frequency of the interconnected system. Finally, the simulation results are provided to verify the correctness of the proposed theoretical analysis.

Abstract Vectorization is a commonly used technique for converting raster images to vector format and has long been a research focus in computer graphics and vision. While a number of attempts have been made to extract the topology of line drawings and further convert them to vector representations, the existing methods commonly focused on resolving junctions composed of thin lines. They usually fail for line drawings composed of thick lines, especially at junctions. In this paper, we propose an automatic line drawing vectorization method that can reconstruct the topology of line drawings of arbitrary thickness. Our key observation is that no matter the lines are thin or thick, the boundaries of the lines always provide reliable hints for reconstructing the topology. For example, the boundaries of two continuous line segments at a junction are usually smoothly connected. By analyzing the continuity of boundaries, we can better analyze the topology at junctions. In particular, we first extract the skeleton of the input line drawing via thinning. Then we analyze the reliability of the skeleton points based on boundaries. Reliable skeleton points are preserved while unreliable skeleton points are reconstructed based on boundaries again. Finally, the skeleton after reconstruction is vectorized as the output. We apply our method on line drawings of various contents and styles. Satisfying results are obtained. Our method significantly outperforms existing methods for line drawings composed of thick lines.

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 textbased 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.

Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i.e., the areas where the user scribbles should propagate and influence. This framework explicitly controls such scribble influence areas for artists to manipulate the colors of image details and avoid color leakage/contamination between scribbles, and simultaneously, leverages data-driven color generation to facilitate content creation. This framework is based on a Split Filling Mechanism (SFM), which first splits the user scribbles into individual groups and then independently processes the colors and influence areas of each group with a Convolutional Neural Network (CNN). Learned from more than a million illustrations, the framework can estimate the scribble influence areas in a content-aware manner, and can smartly generate visually pleasing colors to assist the daily works of artists. We show that our proposed framework is easy to use, allowing even amateurs to obtain professional-quality results on a wide variety of line arts.

Manga inpainting fills up the disoccluded pixels due to the removal of dialogue balloons or "sound effect" text. This process is long needed by the industry for the language localization and the conversion to animated manga. It is mostly done manually, as existing methods (mostly for natural image inpainting) cannot produce satisfying results. Manga inpainting is more tricky than natural image inpainting because its highly abstract illustration using structural lines and screentone patterns, which confuses the semantic interpretation and visual content synthesis. In this paper, we present the first manga inpainting method, a deep learning model, that generates high-quality results. Instead of direct inpainting, we propose to separate the complicated inpainting into two major phases, semantic inpainting and appearance synthesis. This separation eases both the feature understanding and hence the training of the learning model. A key idea is to disentangle the structural line and screentone, that helps the network to better distinguish the structural line and the screentone features for semantic interpretation. Both the visual comparison and the quantitative experiments evidence the effectiveness of our method and justify its superiority over existing state-of-the-art methods in the application of manga inpainting.

Western color comics and Japanese-style screened manga are two popular comic styles. They mainly differ in the style of region-filling. However, the conversion between the two region-filling styles is very challenging, and manually done currently. In this paper, we identify that the major obstacle in the conversion between the two filling styles stems from the difference between the fundamental properties of screened region-filling and colored region-filling. To resolve this obstacle, we propose a screentone variational autoencoder, ScreenVAE, to map the screened manga to an intermediate domain. This intermediate domain can summarize local texture characteristics and is interpolative. With this domain, we effectively unify the properties of screening and color-filling, and ease the learning for bidirectional translation between screened manga and color comics. To carry out the bidirectional translation, we further propose a network to learn the translation between the intermediate domain and color comics. Our model can generate quality screened manga given a color comic, and generate color comic that retains the original screening intention by the bitonal manga artist. Several results are shown to demonstrate the effectiveness and convenience of the proposed method. We also demonstrate how the intermediate domain can assist other applications such as manga inpainting and photo-to-comic conversion.

Abstract Vectorizing line drawing is necessary for the digital workflows of 2D animation and engineering design. But it is challenging due to the ambiguity of topology, especially at junctions. Existing vectorization methods either suffer from low accuracy or cannot deal with high‐resolution images. To deal with a variety of challenging containing different kinds of complex junctions, we propose a two‐phase line drawing vectorization method that analyzes the global and local topology. In the first phase, we subdivide the lines into partial curves, and in the second phase, we reconstruct the topology at junctions. With the overall topology estimated in the two phases, we can trace and vectorize the curves. To qualitatively and quantitatively evaluate our method and compare it with the existing methods, we conduct extensive experiments on not only existing datasets but also our newly synthesized dataset which contains different types of complex and ambiguous junctions. Experimental statistics show that our method greatly outperforms existing methods in terms of computational speed and achieves visually better topology reconstruction accuracy.

To share the same visual content between color vision deficiencies (CVD) and normal-vision people, attempts have been made to allocate the two visual experiences of a binocular display (wearing and not wearing glasses) to CVD and normal-vision audiences. However, existing approaches only work for still images. Although state-of-the-art temporal filtering techniques can be applied to smooth the per-frame generated content, they may fail to maintain the multiple binocular constraints needed in our applications, and even worse, sometimes introduce color inconsistency (same color regions map to different colors). In this paper, we propose to train a neural network to predict the temporal coherent polynomial coefficients in the domain of global color decomposition. This indirect formulation solves the color inconsistency problem. Our key challenge is to design a neural network to predict the temporal coherent coefficients, while maintaining all required binocular constraints. Our method is evaluated on various videos and all metrics confirm that it outperforms all existing solutions.

Sketch or line art colorization is a research field with significant market demand. Different from photo colorization which strongly relies on texture information, sketch colorization is more challenging as sketches may not have texture. Even worse, color, texture, and gradient have to be generated from the abstract sketch lines. In this paper, we propose a semi-automatic learning-based framework to colorize sketches with proper color, texture as well as gradient. Our framework consists of two stages. In the first drafting stage, our model guesses color regions and splashes a rich variety of colors over the sketch to obtain a color draft. In the second refinement stage, it detects the unnatural colors and artifacts, and try to fix and refine the result. Comparing to existing approaches, this two-stage design effectively divides the complex colorization task into two simpler and goal-clearer subtasks. This eases the learning and raises the quality of colorization. Our model resolves the artifacts such as water-color blurring, color distortion, and dull textures. We build an interactive software based on our model for evaluation. Users can iteratively edit and refine the colorization. We evaluate our learning model and the interactive system through an extensive user study. Statistics shows that our method outperforms the state-of-art techniques and industrial applications in several aspects including, the visual quality, the ability of user control, user experience, and other metrics.

Due to the lack of color in manga (Japanese comics), black-and-white textures are often used to enrich visual experience. With the rising need to digitize manga, segmenting texture regions from manga has become an indispensable basis for almost all manga processing, from vectorization to colorization. Unfortunately, such texture segmentation is not easy since textures in manga are composed of lines and exhibit similar features to structural lines (contour lines). So currently, texture segmentation is still manually performed, which is labor-intensive and time-consuming. To extract a texture region, various texture features have been proposed for measuring texture similarity, but precise boundaries cannot be achieved since boundary pixels exhibit different features from inner pixels. In this paper, we propose a novel method which also adopts texture features to estimate texture regions. Unlike existing methods, the estimated texture region is only regarded an initial, imprecise texture region. We expand the initial texture region to the precise boundary based on local smoothness via a graph-cut formulation. This allows our method to extract texture regions with precise boundaries. We have applied our method to various manga images and satisfactory results were achieved in all cases.

Extraction of structural lines from pattern-rich manga is a crucial step for migrating legacy manga to digital domain. Unfortunately, it is very challenging to distinguish structural lines from arbitrary, highly-structured, and black-and-white screen patterns. In this paper, we present a novel data-driven approach to identify structural lines out of pattern-rich manga, with no assumption on the patterns. The method is based on convolutional neural networks. To suit our purpose, we propose a deep network model to handle the large variety of screen patterns and raise output accuracy. We also develop an efficient and effective way to generate a rich set of training data pairs. Our method suppresses arbitrary screen patterns no matter whether these patterns are regular, irregular, tone-varying, or even pictorial, and regardless of their scales. It outputs clear and smooth structural lines even if these lines are contaminated by and immersed in complex patterns. We have evaluated our method on a large number of mangas of various drawing styles. Our method substantially outperforms state-of-the-art methods in terms of visual quality. We also demonstrate its potential in various manga applications, including manga colorization, manga retargeting, and 2.5D manga generation.