Most current Light Field Salient Object Detection (LFSOD) methods require full supervision with labor-intensive pixel-level annotations. Unsupervised Light Field Salient Object Detection (ULFSOD) has gained attention due to this limitation. However, existing methods use traditional handcrafted techniques to generate noisy pseudo-labels, which degrades the performance of models trained on them. To mitigate this issue, we present a novel learning-based approach to synthesize labels for ULFSOD. We introduce a prominent focal stack identification module that utilizes light field information (focal stack, depth map, and RGB color image) to generate high-quality pixel-level pseudo-labels, aiding network training. Additionally, we propose a novel model architecture for LFSOD, combining a multi-scale spatial attention module for focal stack information with a cross fusion module for RGB and focal stack integration. Through extensive experiments, we demonstrate that our pseudo-label generation method significantly outperforms existing methods in label quality. Our proposed model, trained with our labels, shows significant improvement on ULFSOD, achieving new state-of-the-art scores across public benchmarks.

The past few years have witnessed an emergent interest in building generative models for the graphic design domain. For adoption of powerful deep generative models with Transformer-based neural backbones, prior approaches formulate designs as ordered sequences of elements, and simply order the elements in a random or raster manner. We argue that such naive ordering methods are sub-optimal and there is room for improving sample quality through a better choice of order between graphic design elements. In this paper, we seek to explore the space of orderings to find the ordering strategy that optimizes the performance of graphic design generation models. For this, we propose a model, namely G enerative O rder L earner (GOL), which trains an autoregressive generator on design sequences, jointly with an ordering network that sort design elements to maximize the generation quality. With unsupervised training on vector graphic design data, our model is capable of learning a content-adaptive ordering approach, called neural order. Our experiments show that the generator trained with our neural order converges faster, achieving remarkably improved generation quality compared with using alternative ordering baselines. We conduct comprehensive analysis of our learned order to have a deeper understanding of its ordering behaviors. In addition, our learned order can generalize well to diffusion-based generative models and help design generators scale up excellently.

High-resolution images come with rich color information and texture details. Due to the rapid upgrading of display devices and rendering technologies, high-resolution real-time rendering faces the computational overhead challenge. To address this, the current mainstream solution is to render at a lower resolution and then upsample to the target resolution by supersampling techniques. However, while many prior supersampling approaches have attempted to exploit rich rendered data such as color, depth, motion vectors at low resolution, there is little discussion on how to harness high-frequency information that is readily available in the high-resolution (HR) G-buffers of modern renders. In this article, we seek to investigate how to fully leverage information from HR G-buffers to maximize the visual quality of supersampling results. We propose a neural network for real-time supersampling of rendered content, which is based on several core designs, including gated G-buffers encoder, G-buffers attended encoder and reflection-aware loss. These designs are especially made for the sake of effectively using HR G-buffers, enabling faithful recovery of a variety of high-frequency scene details from low-resolution, highly aliased inputs. Furthermore, a simple occlusion-aware blender is proposed to efficiently rectify dis-occluded features in the warped previous frame, allowing us to better exploit history information to improve temporal stability. The experiments show that our method, equipped with strong ability to harness HR G-buffer information, significantly improves the visual fidelity of high-resolution reconstructions upon previous state-of-the-art methods, even for challenging $4 \times 4$4×4 upsampling, while still being compute-efficient.

Designing cinematic compositions, which involves moving cameras through a scene, is essential yet challenging in filmmaking. Machinima filmmaking provides real-time virtual environments for exploring different compositions flexibly and efficiently. However, producing high-quality cinematic compositions in such environments still requires significant cinematography skills and creativity. This paper presents Cinemassist, a tool designed to support and enhance this creative process by generating a variety of cinematic composition proposals at both keyframe and scene levels, which users can incorporate into their workflows and achieve more creative results. At the crux of our system is a deep generative model trained on real movie data, which can generate plausible, diverse camera poses conditioned on 3D animations and additional input semantics. Our model enables an interactive cinematic composition design workflow where users can co-design with the model by being inspired by model-generated suggestions while having control over the generation process. Our user study and expert rating find Cinemassist can facilitate the design process for users of different backgrounds and enhance the design quality especially for users with animation expertise, demonstrating its potential as an invaluable tool in the context of digital filmmaking.

Teleoperation systems find many applications from earlier search-and-rescue to more recent daily tasks. It is widely acknowledged that using external sensors can decouple the view of the remote scene from the motion of the robot arm during manipulation, facilitating the control task. However, this design requires the coordination of multiple operators or may exhaust a single operator as s/he needs to control both the manipulator arm and the external sensors. To address this challenge, our work introduces a viewpoint prediction model, the first data-driven approach that autonomously adjusts the viewpoint of a dynamic camera to assist in telemanipulation tasks. This model is parameterized by a deep neural network and trained on a set of human demonstrations. We propose a contrastive learning scheme that leverages viewpoints in a camera trajectory as contrastive data for network training. We demonstrated the effectiveness of the proposed viewpoint prediction model by integrating it into a real-world robotic system for telemanipulation. User studies reveal that our model outperforms several camera control methods in terms of control experience and reduces the perceived task load compared to manual camera control. As an assistive module of a telemanipulation system, our method significantly reduces task completion time for users who choose to adopt its recommendation.

In recent years, the rapid advancement of image generation techniques has resulted in the widespread abuse of manipulated images, leading to a crisis of trust and affecting social equity. Thus, the goal of our work is to detect and localize tampered regions in images. Many deep learning based approaches have been proposed to address this problem, but they can hardly handle the tampered regions that are manually fine-tuned to blend into image background. By observing that the boundaries of tempered regions are critical to separating tampered and non-tampered parts, we present a novel boundary-guided approach to image manipulation detection, which introduces an inherent bias towards exploiting the boundary information of tampered regions. Our model follows an encoder-decoder architecture, with multi-scale localization mask prediction, and is guided to utilize the prior boundary knowledge through an attention mechanism and contrastive learning. In particular, our model is unique in that 1) we propose a boundary-aware attention module in the network decoder, which predicts the boundary of tampered regions and thus uses it as crucial contextual cues to facilitate the localization; and 2) we propose a multi-scale contrastive learning scheme with a novel boundary-guided sampling strategy, leading to more discriminative localization features. Our state-of-art performance on several public benchmarks demonstrates the superiority of our model over prior works.

With the goal of making contents easy to understand, memorize and share, a clear and easy-to-follow layout is important for visual notes. Unfortunately, since visual notes are often taken by the designers in real time while watching a video or listening to a presentation, the contents are usually not carefully structured, resulting in layouts that may be difficult for others to follow. In this article, we address this problem by proposing a novel approach to automatically optimize the layouts of visual notes. Our approach predicts the design order of a visual note and then warps the contents along the predicted design order such that the visual note can be easier to follow and understand. At the core of our approach is a learning-based framework to reason about the element-wise design orders of visual notes. In particular, we first propose a hierarchical LSTM-based architecture to predict a grid-based design order of the visual note, based on the graphical and textual information. We then derive the element-wise order from the grid-based prediction. Such an idea allows our network to be weakly-supervised, i.e., making it possible to predict dense grid-based orders from visual notes with only coarse annotations. We evaluate the effectiveness of our approach on visual notes with diverse content densities and layouts. The results show that our network can predict plausible design orders for various types of visual notes and our approach can effectively optimize their layouts in order for them to be easier to follow.

Contextual information plays an important role in solving various image and scene understanding tasks. Prior works have focused on the extraction of contextual information from an image and use it to infer the properties of some object(s) in the image or understand the scene behind the image, e.g., context-based object detection, recognition and semantic segmentation. In this paper, we consider an inverse problem, i.e., how to hallucinate the missing contextual information from the properties of standalone objects. We refer to it as object-level scene context prediction. This problem is difficult, as it requires extensive knowledge of the complex and diverse relationships among objects in the scene. We propose a deep neural network, which takes as input the properties (i.e., category, shape, and position) of a few standalone objects to predict an object-level scene layout that compactly encodes the semantics and structure of the scene context where the given objects are. Quantitative experiments and user studies demonstrate that our model can generate more plausible scene contexts than the baselines. Our model also enables the synthesis of realistic scene images from partial scene layouts. Finally, we validate that our model internally learns useful features for scene recognition and fake scene detection.

In this paper, we propose a novel form of weak supervision for salient object detection (SOD) based on saliency bounding boxes, which are minimum rectangular boxes enclosing the salient objects. Based on this idea, we propose a novel weakly-supervised SOD method, by predicting pixel-level pseudo ground truth saliency maps from just saliency bounding boxes. Our method first takes advantage of the unsupervised SOD methods to generate initial saliency maps and addresses the over/under prediction problems, to obtain the initial pseudo ground truth saliency maps. We then iteratively refine the initial pseudo ground truth by learning a multi-task map refinement network with saliency bounding boxes. Finally, the final pseudo saliency maps are used to supervise the training of a salient object detector. Experimental results show that our method outperforms state-of-the-art weakly-supervised methods.

Although huge progress has been made on scene analysis in recent years, most existing works assume the input images to be in day-time with good lighting conditions. In this work, we aim to address the night-time scene parsing (NTSP) problem, which has two main challenges: 1) labeled night-time data are scarce, and 2) over- and under-exposures may co-occur in the input night-time images and are not explicitly modeled in existing pipelines. To tackle the scarcity of night-time data, we collect a novel labeled dataset, named NightCity, of 4,297 real night-time images with ground truth pixel-level semantic annotations. To our knowledge, NightCity is the largest dataset for NTSP. In addition, we also propose an exposure-aware framework to address the NTSP problem through augmenting the segmentation process with explicitly learned exposure features. Extensive experiments show that training on NightCity can significantly improve NTSP performances and that our exposure-aware model outperforms the state-of-the-art methods, yielding top performances on our dataset as well as existing datasets.

When adding a photo onto a graphic design, professional graphic designers often adjust its colors based on some target colors obtained from the brand or product to make the entire design more memorable to audiences and establish a consistent brand identity. However, adjusting the colors of a photo in the context of a graphic design is a difficult task, with two major challenges: (1) Locality: The color is often adjusted locally to preserve the semantics and atmosphere of the original image; and (2) Naturalness: The modified region needs to be carefully chosen and recolored to obtain a semantically valid and visually natural result. To address these challenges, we propose a learning-based approach to photo color adjustment for graphic designs, which maps an input photo along with the target colors to a recolored result. Our method decomposes the color adjustment process into two successive stages: modifiable region selection and target color propagation. The first stage aims to solve the core, challenging problem of which local image region(s) should be adjusted, which requires not only a common sense of colors appearing in our visual world but also understanding of subtle visual design heuristics. To this end, we capitalize on both natural photos and graphic designs to train a region selection network, which detects the most likely regions to be adjusted to the target colors. The second stage trains a recoloring network to naturally propagate the target colors in the detected regions. Through extensive experiments and a user study, we demonstrate the effectiveness of our selective region-based photo recoloring framework.

In this article, we propose a fully automatic system for generating comic books from videos without any human intervention. Given an input video along with its subtitles, our approach first extracts informative keyframes by analyzing the subtitles and stylizes keyframes into comic-style images. Then, we propose a novel automatic multi-page layout framework that can allocate the images across multiple pages and synthesize visually interesting layouts based on the rich semantics of the images (e.g., importance and inter-image relation). Finally, as opposed to using the same type of balloon as in previous works, we propose an emotion-aware balloon generation method to create different types of word balloons by analyzing the emotion of subtitles and audio. Our method is able to vary balloon shapes and word sizes in balloons in response to different emotions, leading to more enriched reading experience. Once the balloons are generated, they are placed adjacent to their corresponding speakers via speaker detection. Our results show that our method, without requiring any user inputs, can generate high-quality comic pages with visually rich layouts and balloons. Our user studies also demonstrate that users prefer our generated results over those by state-of-the-art comic generation systems.

In this paper, we aim to understand the functionality of 2D sketches by predicting how humans would interact with the objects depicted by sketches in real life. Given a 2D sketch, we learn to predict a tactile saliency map for it, which represents where humans would grasp, press, or touch the object depicted by the sketch. We hypothesize that understanding 3D structure and category of the sketched object would help such tactile saliency reasoning. We thus propose to jointly predict the tactile saliency, depth map and semantic category of a sketch in an end-to-end learning-based framework. To train our model, we propose to synthesize training data by leveraging a collection of 3D shapes with 3D tactile saliency information. Experiments show that our model can predict accurate and plausible tactile saliency maps for both synthetic and real sketches. In addition, we also demonstrate that our predicted tactile saliency is beneficial to sketch recognition and sketch-based 3D shape retrieval, and enables us to establish part-based functional correspondences among sketches.

Abstract In this paper, we address RGB‐D salient object detection task by jointly leveraging semantics and contour details of salient objects. We propose a novel semantics‐and‐details complementary fusion network to adaptively integrate cross‐model and multilevel features. Specifically, we employ two kinds of fusion modules in our model, which are designed for fusing high‐level semantic features and integrating contour detail features of the scene components, respectively. The semantics fusion module aggregates high‐level interdependent semantic relationships by a nonlinear weighted summation of small and medium receptive fields. Meanwhile, the details module integrates multi‐level contour detail features to leverage expressive details of salient objects. We achieve new state‐of‐the‐art salient object detection results on seven RGB‐D datasets, that is, STERE, NJU2000, LFSD, NLPR, SSD, DES, and SIP2019 dataset. Experimental results demonstrate that our method outperforms eleven state‐of‐the‐art salient object detection methods.

Contextual information has been shown to be effective in helping solve various image understanding tasks. Previous works have focused on the extraction of contextual information from an image and use it to infer the properties of some object(s) in the image. In this paper, we consider an inverse problem of how to hallucinate missing contextual information from the properties of a few standalone objects. We refer to it as scene context prediction. This problem is difficult as it requires an extensive knowledge of complex and diverse relationships among different objects in natural scenes. We propose a convolutional neural network, which takes as input the properties (i.e., category, shape, and position) of a few standalone objects to predict an object-level scene layout that compactly encodes the semantics and structure of the scene context where the given objects are. Our quantitative experiments and user studies show that our model can generate more plausible scene context than the baseline approach. We demonstrate that our model allows for the synthesis of realistic scene images from just partial scene layouts and internally learns useful features for scene recognition.

Shadow detection is an important and challenging task for scene understanding. Despite promising results from recent deep learning based methods. Existing works still struggle with ambiguous cases where the visual appearances of shadow and non-shadow regions are similar (referred to as distraction in our context). In this paper, we propose a Distraction-aware Shadow Detection Network (DSDNet) by explicitly learning and integrating the semantics of visual distraction regions in an end-to-end framework. At the core of our framework is a novel standalone, differentiable Distraction-aware Shadow (DS) module, which allows us to learn distraction-aware, discriminative features for robust shadow detection, by explicitly predicting false positives and false negatives. We conduct extensive experiments on three public shadow detection datasets, SBU, UCF and ISTD, to evaluate our method. Experimental results demonstrate that our model can boost shadow detection performance, by effectively suppressing the detection of false positives and false negatives, achieving state-of-the-art results.

Buttons are fundamental in web design. An effective button is important for higher click-through and conversion rates. However, designing effective buttons can be challenging for novices. This paper presents a novel interactive method to aid the button design process by making design suggestions. Our method proceeds in three steps: 1) button presence prediction, 2) button layout suggestion and 3) button color selection. We investigate two distinct but complementary interfaces for button design suggestion: 1) region selection interface, where the button will appear in a user-specific region; 2) element selection interface, where the button will be associated with a user-selected element. We compare our method with an existing website building tool, and show that for novice designers, both interfaces require significantly less manual efforts, and produce significantly better button design, as evaluated by professional web designers.

Layout is fundamental to graphic designs. For visual attractiveness and efficient communication of messages and ideas, graphic design layouts often have great variation, driven by the contents to be presented. In this paper, we study the problem of content-aware graphic design layout generation. We propose a deep generative model for graphic design layouts that is able to synthesize layout designs based on the visual and textual semantics of user inputs. Unlike previous approaches that are oblivious to the input contents and rely on heuristic criteria, our model captures the effect of visual and textual contents on layouts, and implicitly learns complex layout structure variations from data without the use of any heuristic rules. To train our model, we build a large-scale magazine layout dataset with fine-grained layout annotations and keyword labeling. Experimental results show that our model can synthesize high-quality layouts based on the visual semantics of input images and keyword-based summary of input text. We also demonstrate that our model internally learns powerful features that capture the subtle interaction between contents and layouts, which are useful for layout-aware design retrieval.

Abstract Web designers often carefully select fonts to fit the context of a web design to make the design look aesthetically pleasing and effective in communication. However, selecting proper fonts for a web design is a tedious and time‐consuming task, as each font has many properties, such as font face, color, and size, resulting in a very large search space. In this paper, we aim to model fonts in context, by studying a novel and challenging problem of predicting fonts that match a given web design. To this end, we propose a novel, multi‐task deep neural network to jointly predict font face, color and size for each text element on a web design, by considering multi‐scale visual features and semantic tags of the web design. To train our model, we have collected a CTXFont dataset, which consists of 1k professional web designs, with labeled font properties. Experiments show that our model outperforms the baseline methods, achieving promising qualitative and quantitative results on the font selection task. We also demonstrate the usefulness of our method in a font selection task via a user study.

Graphic designers often manipulate the overall look and feel of their designs to convey certain personalities (e.g., cute, mysterious and romantic) to impress potential audiences and achieve business goals. However, understanding the factors that determine the personality of a design is challenging, as a graphic design is often a result of thousands of decisions on numerous factors, such as font, color, image, and layout. In this paper, we aim to answer the question of what characterizes the personality of a graphic design. To this end, we propose a deep learning framework for exploring the effects of various design factors on the perceived personalities of graphic designs. Our framework learns a convolutional neural network (called personality scoring network ) to estimate the personality scores of graphic designs by ranking the crawled web data. Our personality scoring network automatically learns a visual representation that captures the semantics necessary to predict graphic design personality. With our personality scoring network, we systematically and quantitatively investigate how various design factors (e.g., color, font, and layout) affect design personality across different scales (from pixels, regions to elements). We also demonstrate a number of practical application scenarios of our network, including element-level design suggestion and example-based personality transfer.

We propose a method for animating still manga imagery through camera movements. Given a series of existing manga pages, we start by automatically extracting panels, comic characters, and balloons from the manga pages. Then, we use a data-driven graphical model to infer per-panel motion and emotion states from low-level visual patterns. Finally, by combining domain knowledge of film production and characteristics of manga, we simulate camera movements over the manga pages, yielding an animation. The results augment the still manga contents with animated motion that reveals the mood and tension of the story, while maintaining the original narrative. We have tested our method on manga series of different genres, and demonstrated that our method can generate animations that are more effective in storytelling and pacing, with less human efforts, as compared with prior works. We also show two applications of our method, mobile comic reading, and comic trailer generation.

We present a novel approach that allows web designers to easily direct user attention via visual flow on web designs. By collecting and analyzing users' eye gaze data on real-world webpages under the task-driven condition, we build two user attention models that characterize user attention patterns between a pair of page components. These models enable a novel web design interaction for designers to easily create a visual flow to guide users' eyes (i.e., direct user attention along a given path) through a web design with minimal effort. In particular, given an existing web design as well as a designer-specified path over a subset of page components, our approach automatically optimizes the web design so that the resulting design can direct users' attention to move along the input path. We have tested our approach on various web designs of different categories. Results show that our approach can effectively guide user attention through the web design according to the designer's high-level specification.

Automatically extracting frames/panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas. However, automatic panel extraction for manga, i.e., Japanese comics, can be especially challenging, largely because of its complex panel layout design mixed with various visual symbols throughout the page. In this paper, we propose a robust method for automatically extracting panels from digital manga pages. Our method first extracts the panel block by closing open panels and identifying a page background mask. It then performs a recursive binary splitting to partition the panel block into a set of sub-blocks, where an optimal splitting line at each recursive level is determined adaptively.

Picture subjects and text balloons are basic elements in comics, working together to propel the story forward. Japanese comics artists often leverage a carefully designed composition of subjects and balloons (generally referred to as panel elements ) to provide a continuous and fluid reading experience. However, such a composition is hard to produce for people without the required experience and knowledge. In this paper, we propose an approach for novices to synthesize a composition of panel elements that can effectively guide the reader's attention to convey the story. Our primary contribution is a probabilistic graphical model that describes the relationships among the artist's guiding path, the panel elements, and the viewer attention, which can be effectively learned from a small set of existing manga pages. We show that the proposed approach can measurably improve the readability, visual appeal, and communication of the story of the resulting pages, as compared to an existing method. We also demonstrate that the proposed approach enables novice users to create higher-quality compositions with less time, compared with commercially available programs.

Manga layout is a core component in manga production, characterized by its unique styles. However, stylistic manga layouts are difficult for novices to produce as it requires hands-on experience and domain knowledge. In this paper, we propose an approach to automatically generate a stylistic manga layout from a set of input artworks with user-specified semantics, thus allowing less-experienced users to create high-quality manga layouts with minimal efforts. We first introduce three parametric style models that encode the unique stylistic aspects of manga layouts, including layout structure, panel importance, and panel shape. Next, we propose a two-stage approach to generate a manga layout: 1) an initial layout is created that best fits the input artworks and layout structure model, according to a generative probabilistic framework; 2) the layout and artwork geometries are jointly refined using an efficient optimization procedure, resulting in a professional-looking manga layout. Through a user study, we demonstrate that our approach enables novice users to easily and quickly produce higher-quality layouts that exhibit realistic manga styles, when compared to a commercially-available manual layout tool.