Facial Feature Exaggeration According to Social Psychology of Face Perception

We propose a personality trait exaggeration system emphasizing the impression of human face in images, based on multi‐level features learning and exaggeration. These features are called Personality Trait Model (PTM). Abstract level of PTM is social psychology trait of face perception such as amiable, mean, cute and so on. Concrete level of PTM is shape feature and texture feature. A training phase is presented to learn multi‐level features of faces from different images. Statistical survey is taken to label sample images with people's first impressions. From images with the same labels, we capture not only shape features but also texture features to enhance exaggeration effect. Texture feature is expressed by matrix to reflect depth of facial organs, wrinkles and so on. In application phase, original images will be exaggerated using PTM iteratively. And exaggeration rate for each iteration is constrained to keep likeness with the original face. Experimental results demonstrate that our system can emphasize chosen social psychology traits effectively.     

Visibility Equalizer Cutaway Visualization of Mesoscopic Biological Models

In scientific illustrations and visualization, cutaway views are often employed as an effective technique for occlusion management in densely packed scenes. We propose a novel method for authoring cutaway illustrations of mesoscopic biological models. In contrast to the existing cutaway algorithms, we take advantage of the specific nature of the biological models. These models consist of thousands of instances with a comparably smaller number of different types. Our method constitutes a two stage process. In the first step, clipping objects are placed in the scene, creating a cutaway visualization of the model. During this process, a hierarchical list of stacked bars inform the user about the instance visibility distribution of each individual molecular type in the scene. In the second step, the visibility of each molecular type is fine‐tuned through these bars, which at this point act as interactive visibility equalizers. An evaluation of our technique with domain experts confirmed that our equalizer‐based approach for visibility specification is valuable and effective for both, scientific and educational purposes.     

Trip Synopsis: 60km in 60sec

Computerized route planning tools are widely used today by travelers all around the globe, while 3D terrain and urban models are becoming increasingly elaborate and abundant. This makes it feasible to generate a virtual 3D flyby along a planned route. Such a flyby may be useful, either as a preview of the trip, or as an after‐the‐fact visual summary. However, a naively generated preview is likely to contain many boring portions, while skipping too quickly over areas worthy of attention. In this paper, we introduce 3D trip synopsis: a continuous visual summary of a trip that attempts to maximize the total amount of visual interest seen by the camera. The main challenge is to generate a synopsis of a prescribed short duration, while ensuring a visually smooth camera motion. Using an application‐specific visual interest metric, we measure the visual interest at a set of viewpoints along an initial camera path, and maximize the amount of visual interest seen in the synopsis by varying the speed along the route. A new camera path is then computed using optimization to simultaneously satisfy requirements, such as smoothness, focus and distance to the route. The process is repeated until convergence. The main technical contribution of this work is a new camera control method, which iteratively adjusts the camera trajectory and determines all of the camera trajectory parameters, including the camera position, altitude, heading, and tilt. Our results demonstrate the effectiveness of our trip synopses, compared to a number of alternatives.     

Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High‐dimensional Data

The visual analysis of multivariate projections is a challenging task, because complex visual structures occur. This causes fatigue or misinterpretations, which distorts the analysis. In fact, the same projection can lead to different analysis results. We provide visual guidance pictograms to improve objectivity of the visual search. A visual guidance pictogram is an iconic visual density map encoding the visual structure of certain data properties. By using them to guide the analysis, structures in the projection can be better understood and mentally linked to properties in the data. We introduce a systematic scheme for designing such pictograms and provide a set of pictograms for standard visual tasks, such as correlation and distribution analysis, for standard projections like scatterplots, RadVis, and Star Coordinates. We conduct a study that compares the visual analysis of real data with and without the support of guidance pictograms. Our tests show that the training effort for a visual search can be decreased and the analysis bias can be reduced by supporting the user's visual search with guidance pictograms.     

Efficient Contrast Effect Compensation with Personalized Perception Models

Color is one of the most effective visual variables and is frequently used to encode metric quantities. Contrast effects are considered harmful in data visualizations since they significantly bias our perception of colors. For instance, a gray patch appears brighter on a black background than on a white background. Accordingly, the perception of color‐encoded data items depends on the surround in the rendered visualization. A method that compensates for contrast effects has been presented previously, which significantly improves the users’ accuracy in reading and comparing color encoded data. The method utilizes established perception models to compensate for contrast effects, assuming an average human observer. In this paper, we provide experiments that show a significant difference in the perception of users. We introduce methods to personalize contrast effect compensation and show that this outperforms the original method with a user study. We, further, overcome the major limitation of the original method, which is a runtime of several minutes. With the use of efficient optimization and surrogate models, we are able to reduce runtime to milliseconds, making the method applicable in interactive visualizations.     

Panoramic Video from Unstructured Camera Arrays

We describe an algorithm for generating panoramic video from unstructured camera arrays. Artifact‐free panorama stitching is impeded by parallax between input views. Common strategies such as multi‐level blending or minimum energy seams produce seamless results on quasi‐static input. However, on video input these approaches introduce noticeable visual artifacts due to lack of global temporal and spatial coherence. In this paper we extend the basic concept of local warping for parallax removal. Firstly, we introduce an error measure with increased sensitivity to stitching artifacts in regions with pronounced structure. Using this measure, our method efficiently finds an optimal ordering of pair‐wise warps for robust stitching with minimal parallax artifacts. Weighted extrapolation of warps in non‐overlap regions ensures temporal stability, while at the same time avoiding visual discontinuities around transitions between views. Remaining global deformation introduced by the warps is spread over the entire panorama domain using constrained relaxation, while staying as close as possible to the original input views. In combination, these contributions form the first system for spatiotemporally stable panoramic video stitching from unstructured camera array input.     

Re‐Compositable Panoramic Selfie with Robust Multi‐Frame Segmentation and Stitching

It is a challenging task for ordinary users to capture selfies with a good scene composition, given the limited freedom to position the camera. Creative hardware (e.g., selfie sticks) and software (e.g., panoramic selfie apps) solutions have been proposed to extend the background coverage of a selife, but to achieve a perfect composition on the spot when the selfie is captured remains to be difficult. In this paper, we propose a system that allows the user to shoot a selfie video by rotating the body first, then produce a final panoramic selfie image with user‐guided scene composition as postprocessing. Our key technical contribution is a fully Automatic, robust multi‐frame segmentation and stitching framework that is tailored towards the special characteristics of selfie images. We analyze the sparse feature points and employ a spatial‐temporal optimization for bilayer feature segmentation, which leads to more reliable background alignment than previous image stitching techniques. The sparse classification is then propagated to all pixels to create dense foreground masks for person‐background composition. Finally, based on a user‐selected foreground position, our system uses content‐preserving warping to produce a panoramic seflie with minimal distortion to the face region. Experimental results show that our approach can reliably generate high quality panoramic selfies, while a simple combination of previous image stitching and segmentation approaches often fails.     

Parallel Marching Blocks: A Practical Isosurfacing Algorithm for Large Data on Many‐Core Architectures

Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state‐of‐the‐art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limitations on available GPU memory mean that they are unable to deal with the larger datasets that are now increasingly becoming prevalent. This paper proposes a new parallel isosurface‐extraction algorithm that exploits the blocked organisation of the parallel threads found in modern many‐core platforms to achieve fast isosurface extraction and reduce the associated memory requirements. This is achieved by optimising thread co‐operation within thread‐blocks and reducing redundant computation; ultimately, an indexed triangular mesh can be produced. Experiments have shown that the proposed algorithm is much faster (up to 10×) than state‐of‐the‐art GPU algorithms and has a much smaller memory footprint, enabling it to handle much larger datasets (up to 64×) on the same GPU.     

Efficient Depth Propagation for Constructing a Layered Depth Image from a Single Image

In this paper, we propose an interactive technique for constructing a 3D scene via sparse user inputs. We represent a 3D scene in the form of a Layered Depth Image (LDI) which is composed of a foreground layer and a background layer, and each layer has a corresponding texture and depth map. Given user‐specified sparse depth inputs, depth maps are computed based on superpixels using interpolation with geodesic‐distance weighting and an optimization framework. This computation is done immediately, which allows the user to edit the LDI interactively. Additionally, our technique automatically estimates depth and texture in occluded regions using the depth discontinuity. In our interface, the user paints strokes on the 3D model directly. The drawn strokes serve as 3D handles with which the user can pull out or push the 3D surface easily and intuitively with real‐time feedback. We show our technique enables efficient modeling of LDI that produce sufficient 3D effects.     

Methods for Compensating Contrast Effects in Information Visualization

Color, as one of the most effective visual variables, is used in many techniques to encode and group data points according to different features. Relations between features and groups appear as visual patterns in the visualization. However, optical illusions may bias the perception at the first level of the analysis process. For instance, in pixel‐based visualizations contrast effects make pixels appear brighter if surrounded by a darker area, which distorts the encoded metric quantity of the data points. Even if we are aware of these perceptual issues, our visual cognition system is not able to compensate these effects accurately. To overcome this limitation, we present a color optimization algorithm based on perceptual metrics and color perception models to reduce physiological contrast or color effects. We evaluate our technique with a user study and find that the technique doubles the accuracy of users comparing and estimating color encoded data values. Since the presented technique can be used in any application without adaption to the visualization itself, we are able to demonstrate its effectiveness on data visualizations in different domains.     

Multiple Scattering Approximation in Heterogeneous Media by Narrow Beam Distributions

Fast realistic rendering of objects in scattering media is still a challenging topic in computer graphics. In presence of participating media, a light beam is repeatedly scattered by media particles, changing direction and getting spread out. Explicitly evaluating this beam distribution would enable efficient simulation of multiple scattering events without involving costly stochastic methods. Narrow beam theory provides explicit equations that approximate light propagation in a narrow incident beam. Based on this theory, we propose a closed‐form distribution function for scattered beams. We successfully apply it to the image synthesis of scenes in which scattering occurs, and show that our proposed estimation method is more accurate than those based on the Wentzel‐Kramers‐Brillouin (WKB) theory.     

Hardware‐Based Non‐Photorealistic Rendering Using a Painting Robot

We describe a painting machine and associated algorithms. Our modified industrial robot works with visual feedback and applies acrylic paint from a repository to a canvas until the created painting resembles a given input image or scene. The color differences between canvas and input are used to direct the application of new strokes. We present two optimization‐based algorithms that place such strokes in relation to already existing ones. Using these methods we are able to create different painting styles, one that tries to match the input colors with almost transparent strokes and another one that creates dithering patterns of opaque strokes that approximate the input color. The machine produces paintings that mimic those created by human painters and allows us to study the painting process as well as the creation of artworks.     

Real‐time Texture Synthesis and Concurrent Random‐access Rendering for Low‐cost GPU Chip Design

Numerous algorithms have been researched in the area of texture synthesis. However, it remains difficult to design a low‐cost synthesis scheme capable of generating high quality results while simultaneously achieving real‐time performance. Additional challenges include making a scheme parallel and being able to partially render/synthesize high‐resolution textures. Furthermore, it would be beneficial for a synthesis scheme to be able to incorporate Texture Compression and minimize the bandwidth usage, especially on mobile devices. In this paper, we propose a practical method which has low computational complexity and produces textures with small storage requirements. Through use of an index table, random access of the texture is another essential advantage, with which parallel rendering becomes feasible including generation of mip‐map sequences. Integrating the index table with existing compression algorithms, for example ETC or PVRTC, the bandwidth is further reduced and avoids the need for a separate, computationally expensive pass to compress the synthesized output. It should be noted that our texture synthesis achieves real‐time performance and low power consumption even on mobile devices, for which texture synthesis has been traditionally considered too expensive.     

Efficient Gradient‐Domain Compositing Using an Approximate Curl‐free Wavelet Projection

Gradient‐domain compositing has been widely used to create a seamless composite with gradient close to a composite gradient field generated from one or more registered images. The key to this problem is to solve a Poisson equation, whose unknown variables can reach the size of the composite if no region of interest is drawn explicitly, thus making both the time and memory cost expensive in processing multi‐megapixel images. In this paper, we propose an approximate projection method based on biorthogonal Multiresolution Analyses (MRA) to solve the Poisson equation. Unlike previous Poisson equation solvers which try to converge to the accurate solution with iterative algorithms, we use biorthogonal compactly supported curl‐free wavelets as the fundamental bases to approximately project the composite gradient field onto a curl‐free vector space. Then, the composite can be efficiently recovered by applying a fast inverse wavelet transform. Considering an n‐pixel composite, our method only requires 2n of memory for all vector fields and is more efficient than state‐of‐the‐art methods while achieving almost identical results. Specifically, experiments show that our method gains a 5× speedup over the streaming multigrid in certain cases.     

Appearance Harmonization for Single Image Shadow Removal

Shadow removal is a challenging problem and previous approaches often produce de‐shadowed regions that are visually inconsistent with the rest of the image. We propose an automatic shadow region harmonization approach that makes the appearance of a de‐shadowed region (produced using any previous technique) compatible with the rest of the image. We use a shadow‐guided patch‐based image synthesis approach that reconstructs the shadow region using patches sampled from non‐shadowed regions. This result is then refined based on the reconstruction confidence to handle unique textures. Qualitative comparisons over a wide range of images, and a quantitative evaluation on a benchmark dataset show that our technique significantly improves upon the state‐of‐the‐art.     

Evaluating the Quality of Face Alignment without Ground Truth

The study of face alignment has been an area of intense research in computer vision, with its achievements widely used in computer graphics applications. The performance of various face alignment methods is often image‐dependent or somewhat random because of their own strategy. This study aims to develop a method that can select an input image with good face alignment results from many results produced by a single method or multiple ones. The task is challenging because different face alignment results need to be evaluated without any ground truth. This study addresses this problem by designing a feasible feature extraction scheme to measure the quality of face alignment results. The feature is then used in various machine learning algorithms to rank different face alignment results. Our experiments show that our method is promising for ranking face alignment results and is able to pick good face alignment results, which can enhance the overall performance of a face alignment method with a random strategy. We demonstrate the usefulness of our ranking‐enhanced face alignment algorithm in two practical applications: face cartoon stylization and digital face makeup.     

Motion Aware Exposure Bracketing for HDR Video

Mobile phones and tablets are rapidly gaining significance as omnipresent image and video capture devices. In this context we present an algorithm that allows such devices to capture high dynamic range (HDR) video. The design of the algorithm was informed by a perceptual study that assesses the relative importance of motion and dynamic range. We found that ghosting artefacts are more visually disturbing than a reduction in dynamic range, even if a comparable number of pixels is affected by each. We incorporated these findings into a real‐time, adaptive metering algorithm that seamlessly adjusts its settings to take exposures that will lead to minimal visual artefacts after recombination into an HDR sequence. It is uniquely suitable for real‐time selection of exposure settings. Finally, we present an off‐line HDR reconstruction algorithm that is matched to the adaptive nature of our real‐time metering approach.     

Single Image Weathering via Exemplar Propagation

This paper presents an efficient approach for generating weathering effects with detailed appearance variations in a single image. Previous approaches merely change chroma or reflectance of weathered objects, which is not sufficient for materials with detailed shading and texture variations, such as growing moss and peeling plaster. Our method propagates such detailed features via seamless patch‐based synthesis driven by weathering degree distribution. Unlike previous methods, the weathering degrees are calculated efficiently using Radial Basis Functions even for materials with wide color variations. We use graph cut‐based optimization to identify the most weathered region as a “weathering exemplar”, from which we sample weathering patches. We demonstrate our method enables us to generate various types of detailed weathering effects interactively.     

Efficient Variational Light Field View Synthesis For Making Stereoscopic 3D Images

We present a novel approach for making stereoscopic images by variational view synthesis on the multi‐perspective light field. With the intended disparities as constraints, we specialize the generative variational model by incorporating per‐pixel viewpoint assignment to synthesize the stereo pair. Also, we improve the variational solution by use of explicit weighted average on the light field. Our algorithm is able to handle arbitrary disparity remapping, thus enabling more flexible disparity control for the desired stereoscopic effect. The experiments demonstrate the effectiveness and efficiency for making the stereoscopic 3D images based on the light field.