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Now showing 1 - 10 of 19
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    A Practical Approach to Physically-Based Reproduction of Diffusive Cosmetics
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Kim, Goanghun; Ko, Hyeong-Seok; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    In this paper, we introduce so-called the bSX method as a new way to utilize the Kubelka-Munk (K-M) model. Assuming the material is completely diffusive, the K-M model gives the reflectance and transmittance of the material from the observation of the material applied on a backing, where the observation includes the thickness of the material application. By rearranging the original K-M equation, we propose that the reflectance and transmittance can be calculated without knowing the thickness. This is a practically useful contribution. Based on the above finding, we develop the bSX method which can (1) capture the material specific parameters from the two photos - taken before and after the material application, and (2) reproduce its effect on a novel backing. We experimented the proposed method in various cases related to virtual cosmetic try-on, which include (1) capture from a single color backing, (2) capture from human skin backing, (3) reproduction of varying thickness effect, (4) reproduction of multi-layer cosmetic application effect, (5) applying the proposed method to makeup transfer. Compared to previous image-based makeup transfer methods, the bSX method reproduces the feel of the cosmetics more accurately.
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    ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Marnerides, Demetris; Bashford-Rogers, Thomas; Hatchett, Jon; Debattista, Kurt; Gutierrez, Diego and Sheffer, Alla
    High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.
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    Bayesian Collaborative Denoising for Monte Carlo Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2017) Boughida, Malik; Boubekeur, Tamy; Zwicker, Matthias and Sander, Pedro
    The stochastic nature of Monte Carlo rendering algorithms inherently produces noisy images. Essentially, three approaches have been developed to solve this issue: improving the ray-tracing strategies to reduce pixel variance, providing adaptive sampling by increasing the number of rays in regions needing so, and filtering the noisy image as a post-process. Although the algorithms from the latter category introduce bias, they remain highly attractive as they quickly improve the visual quality of the images, are compatible with all sorts of rendering effects, have a low computational cost and, for some of them, avoid deep modifications of the rendering engine. In this paper, we build upon recent advances in both non-local and collaborative filtering methods to propose a new efficient denoising operator for Monte Carlo rendering. Starting from the local statistics which emanate from the pixels sample distribution, we enrich the image with local covariance measures and introduce a nonlocal bayesian filter which is specifically designed to address the noise stemming from Monte Carlo rendering. The resulting algorithm only requires the rendering engine to provide for each pixel a histogram and a covariance matrix of its color samples. Compared to state-of-the-art sample-based methods, we obtain improved denoising results, especially in dark areas, with a large increase in speed and more robustness with respect to the main parameter of the algorithm. We provide a detailed mathematical exposition of our bayesian approach, discuss extensions to multiscale execution, adaptive sampling and animated scenes, and experimentally validate it on a collection of scenes.
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    Terrain Super-resolution through Aerial Imagery and Fully Convolutional Networks
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Argudo, Oscar; Chica, Antonio; Andujar, Carlos; Gutierrez, Diego and Sheffer, Alla
    Despite recent advances in surveying techniques, publicly available Digital Elevation Models (DEMs) of terrains are lowresolution except for selected places on Earth. In this paper we present a new method to turn low-resolution DEMs into plausible and faithful high-resolution terrains. Unlike other approaches for terrain synthesis/amplification (fractal noise, hydraulic and thermal erosion, multi-resolution dictionaries), we benefit from high-resolution aerial images to produce highly-detailed DEMs mimicking the features of the real terrain. We explore different architectures for Fully Convolutional Neural Networks to learn upsampling patterns for DEMs from detailed training sets (high-resolution DEMs and orthophotos), yielding up to one order of magnitude more resolution. Our comparative results show that our method outperforms competing data amplification approaches in terms of elevation accuracy and terrain plausibility.
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    Watercolor Woodblock Printing with Image Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Panotopoulou, Athina; Paris, Sylvain; Whiting, Emily; Gutierrez, Diego and Sheffer, Alla
    Watercolor paintings have a unique look that mixes subtle color gradients and sophisticated diffusion patterns. This makes them immediately recognizable and gives them a unique appeal. Creating such paintings requires advanced skills that are beyond the reach of most people. Even for trained artists, producing several copies of a painting is a tedious task. One can resort to scanning an existing painting and printing replicas, but these are all identical and have lost an essential characteristic of a painting, its uniqueness. We address these two issues with a technique to fabricate woodblocks that we later use to create watercolor prints. The woodblocks can be reused to produce multiple copies but each print is unique due to the physical process that we introduce. We also design an image processing pipeline that helps users to create the woodblocks and describe a protocol that produces prints by carefully controlling the interplay between the paper, ink pigments, and water so that the final piece depicts the desired scene while exhibiting the distinctive features of watercolor. Our technique enables anyone with the resources to produce watercolor prints.
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    Controlling Stroke Size in Fast Style Transfer with Recurrent Convolutional Neural Network
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Yang, Lingchen; Yang, Lumin; Zhao, Mingbo; Zheng, Youyi; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Controlling stroke size in Fast Style Transfer remains a difficult task. So far, only a few attempts have been made towards it, and they still exhibit several deficiencies regarding efficiency, flexibility, and diversity. In this paper, we aim to tackle these problems and propose a recurrent convolutional neural subnetwork, which we call recurrent stroke-pyramid, to control the stroke size in Fast Style Transfer. Compared to the state-of-the-art methods, our method not only achieves competitive results with much fewer parameters but provides more flexibility and efficiency for generalizing to unseen larger stroke size and being able to produce a wide range of stroke sizes with only one residual unit. We further embed the recurrent stroke-pyramid into the Multi-Styles and the Arbitrary-Style models, achieving both style and stroke-size control in an entirely feed-forward manner with two novel run-time control strategies.
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    Binocular Tone Mapping with Improved Overall Contrast and Local Details
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Zhang, Zhuming; Hu, Xinghong; Liu, Xueting; Wong, Tien-Tsin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Tone mapping is a commonly used technique that maps the set of colors in high-dynamic-range (HDR) images to another set of colors in low-dynamic-range (LDR) images, to fit the need for print-outs, LCD monitors and projectors. Unfortunately, during the compression of dynamic range, the overall contrast and local details generally cannot be preserved simultaneously. Recently, with the increased use of stereoscopic devices, the notion of binocular tone mapping has been proposed in the existing research study. However, the existing research lacks the binocular perception study and is unable to generate the optimal binocular pair that presents the most visual content. In this paper, we propose a novel perception-based binocular tone mapping method, that can generate an optimal binocular image pair (generating left and right images simultaneously) from an HDR image that presents the most visual content by designing a binocular perception metric. Our method outperforms the existing method in terms of both visual and time performance.
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    Automatic Mechanism Modeling from a Single Image with CNNs
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Lin, Minmin; Shao, Tianjia; Zheng, Youyi; Ren, Zhong; Weng, Yanlin; Yang, Yin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    This paper presents a novel system that enables a fully automatic modeling of both 3D geometry and functionality of a mechanism assembly from a single RGB image. The resulting 3D mechanism model highly resembles the one in the input image with the geometry, mechanical attributes, connectivity, and functionality of all the mechanical parts prescribed in a physically valid way. This challenging task is realized by combining various deep convolutional neural networks to provide high-quality and automatic part detection, segmentation, camera pose estimation and mechanical attributes retrieval for each individual part component. On the top of this, we use a local/global optimization algorithm to establish geometric interdependencies among all the parts while retaining their desired spatial arrangement. We use an interaction graph to abstract the inter-part connection in the resulting mechanism system. If an isolated component is identified in the graph, our system enumerates all the possible solutions to restore the graph connectivity, and outputs the one with the smallest residual error. We have extensively tested our system with a wide range of classic mechanism photos, and experimental results show that the proposed system is able to build high-quality 3D mechanism models without user guidance.
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    Split-Depth Image Generation and Optimization
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Liao, Jingtang; Eisemann, Martin; Eisemann, Elmar; Jernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornung
    Split-depth images use an optical illusion, which can enhance the 3D impression of a 2D animation. In split-depth images (also often called split-depth GIFs due to the commonly used file format), static virtual occluders in form of vertical or horizontal bars are added to a video clip, which leads to occlusions that are interpreted by the observer as a depth cue. In this paper, we study different factors that contribute to the illusion and propose a solution to generate split-depth images for a given RGB + depth image sequence. The presented solution builds upon a motion summarization of the object of interest (OOI) through space and time. It allows us to formulate the bar positioning as an energy-minimization problem, which we solve efficiently. We take a variety of important features into account, such as the changes of the 3D effect due to changes in the motion topology, occlusion, the proximity of bars or the OOI, and scene saliency. We conducted a number of psycho-visual experiments to derive an appropriate energy formulation. Our method helps in finding optimal positions for the bars and, thus, improves the 3D perception of the original animation. We demonstrate the effectiveness of our approach on a variety of examples. Our study with novice users shows that our approach allows them to quickly create satisfying results even for complex animations.
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    Cosine-Weighted B-Spline Interpolation on the Face-Centered Cubic Lattice
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Rácz, Gergely Ferenc; Csébfalvi, Balázs; Jeffrey Heer and Heike Leitte and Timo Ropinski
    Cosine-Weighted B-spline (CWB) interpolation [Csé13] has been originally proposed for volumetric data sampled on the Body-Centered Cubic (BCC) lattice. The BCC lattice is well known to be optimal for sampling isotropically band-limited signals above the Nyquist limit. However, the Face-Centered Cubic (FCC) lattice has been recently proven to be optimal for low-rate sampling. The CWB interpolation is a state-of-the-art technique on the BCC lattice, which outperforms, for example, the previously proposed box-spline interpolation in terms of both efficiency and visual quality. In this paper, we show that CWB interpolation can be adapted to the FCC lattice as well, and results in similarly isotropic signal reconstructions as on the BCC lattice.