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Now showing 1 - 10 of 19
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    A Multifragment Renderer for Material Aging Visualization
    (The Eurographics Association, 2018) Adamopoulos, Georgios; Moutafidou, Anastasia; Drosou, Anastasios; Tzovaras, Dimitrios; Fudos, Ioannis; Jain, Eakta and Kosinka, Jirí
    People involved in curatorial work and in preservation/conservation tasks need to understand exactly the nature of aging and to prevent it with minimal preservation work. In this scenario, it is of extreme importance to have tools to produce and visualize digital representations and models of visual surface appearance and material properties, to help the scientist understand how they evolve over time and under particular environmental conditions. We report on the development of a multifragment renderer for visualizing and combining the results of simulated aging of artwork objects. Several natural aging processes manifest themselves through change of color, fading, deformations or cracks. Furthermore, changes in the materials underneath the visible layers may be detected or simulated.
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    Audio-driven Emotional Speech Animation
    (The Eurographics Association, 2018) Charalambous, Constantinos; Yumak, Zerrin; Stappen, A. Frank van der; Jain, Eakta and Kosinka, Jirí
    We propose a procedural audio-driven speech animation method that takes into account emotional variations in speech. Given any audio with its corresponding speech transcript, the method generates speech animation for any 3D character. The expressive speech model matches the pitch and intensity variations in audio to individual visemes. In addition, we introduce a dynamic co-articulation model that takes into account linguistic rules varying among emotions. We test our approach against two popular speech animation tools and we show that our method surpass them in a perceptual experiment.
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    From Spectra to Perceptual Color: Visualization Tools for the Dimensional Reduction Achieved by the Human Color Sense
    (The Eurographics Association, 2018) Harvey, Joshua S.; Siviour, Clive R.; Smithson, Hannah E.; Jain, Eakta and Kosinka, Jirí
    Physical colors, defined as unique combinations of photon populations whose wavelengths lie in the visible range, occupy an infinite-dimensional real Hilbert space. Any number of photon populations from the continuous spectrum of monochromatic wavelengths may be present to any positive amount. For normal vision, this space collapses to three dimensions at the retina, with any physical color eliciting just three sensor values corresponding to the excitations of the three classes of cone photoreceptor cells. While there are many mappings and visualizations of three-dimensional perceptual color space, attempts to visualize the relationship between infinite-dimensional physical color space and perceptual space are lacking. We present a visualization framework to illustrate this mathematical relation, using animation and transparency to map multiple physical colors to locations in perceptual space, revealing how the perceptual color solid can be understood as intersecting surfaces and volumes. This framework provides a clear and intuitive illustration of color metamerism.
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    Boundary-aided Human Body Shape and Pose Estimation from a Single Image for Garment Design and Manufacture
    (The Eurographics Association, 2018) Xu, Zongyi; Zhang, Qianni; Jain, Eakta and Kosinka, Jirí
    Current virtual clothing design applications mainly use predefined virtual avatars which are created by professionals. The models are unrealistic as they lack the personalised body shapes and the simulation of human body muscle and soft tissue. To address this problem, we firstly fit the state-of-the-art parametric 3D human body model, SMPL, to 2D joints and boundary of the human body which are detected using CNN methods automatically. Considering the scenario of virtual dressing where people are usually in stable poses, we define a stable pose prior from CMU motion capture (mocap) dataset for further improving accuracy of pose estimation. Accurate estimation of human body shape and poses provides manufacturers and designers with more comprehensive human body measurements, which put a step forwards clothing design and manufacture through Internet.
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    A Drink in Mars: an Approach to Distributed Reality
    (The Eurographics Association, 2018) Perez, Pablo; Gonzalez-Sosa, Ester; Kachach, Redouane; Ruiz, Jaime Jesus; Villegas, Alvaro; Jain, Eakta and Kosinka, Jirí
    We have developed A Drink in Mars application as a proof of concept of Distributed Reality, a particularisation of Mixed Reality which combines a reality transmitted from a remote place (e.g. live immersive video stream from Mars) with user interaction with the local reality (e.g. drink their favourite beverage). The application shows acceptable immersion and local interactivity. It runs on Samsung GearVR and needs no special green room for chroma keying, thus being suitable to test different use cases.
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    Presenting a Deep Motion Blending Approach for Simulating Natural Reach Motions
    (The Eurographics Association, 2018) Gaisbauer, Felix; Froehlich, Philipp; Lehwald, Jannes; Agethen, Philipp; Rukzio, Enrico; Jain, Eakta and Kosinka, Jirí
    Motion blending and character animation systems are widely used in different domains such as gaming or simulation within production industries. Most of the established approaches are based on motion blending techniques. These approaches provide natural motions within common scenarios while inducing low computational costs. However, with increasing amount of influence parameters and constraints such as collision-avoidance, they increasingly fail or require a vast amount of time to meet these requirements. With ongoing progress in artificial intelligence and neural networks, recent works present deep learning based approaches for motion synthesis, which offer great potential for modeling natural motions, while considering heterogeneous influence factors. In this paper, we propose a novel deep blending approach to simulate non-cyclical natural reach motions based on an extension of phase functioned deep neural networks.
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    Introducing a Modular Concept for Exchanging Character Animation Approaches
    (The Eurographics Association, 2018) Gaisbauer, Felix; Agethen, Philipp; Bär, Thomas; Rukzio, Enrico; Jain, Eakta and Kosinka, Jirí
    Nowadays, motion synthesis and character animation systems are used in different domains ranging from gaming to medicine and production industries. In recent years, there has been a vast progress in terms of realistic character animation. In this context, motion-capture based animation systems are frequently used to generate natural motions. Other approaches use physics based simulation, statistical models or machine learning methods to generate realistic motions. These approaches are however tightly coupled with the development environment, thus inducing high porting efforts if being incorporated into different platforms. Currently, no standard exists which allows to exchange complex character animation approaches. A comprehensive simulation of complex scenarios utilizing these heterogeneous approaches is therefore not possible, yet. In a different domain than motion, the Functional Mock-up Interface standard has already solved this problem. Initially being tailored to industrial needs, the standards allows to exchange dynamic simulation approaches such as solvers for mechatronic components. We present a novel concept, extending this standard to couple arbitrary character animation approaches using a common interface.
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    Exemplar Based Filtering of 2.5D Meshes of Faces
    (The Eurographics Association, 2018) Dihl, Leandro; Cruz, Leandro; Gonçalves, Nuno; Jain, Eakta and Kosinka, Jirí
    In this work, we present a content-aware filtering for 2.5D meshes of faces. We propose an exemplar-based filter that corrects each point of a given mesh through local model-exemplar neighborhood comparison. We take advantage of prior knowledge of the models (faces) to improve the comparison. We first detect facial feature points, and create the point correctors for regions of each feature, and only use the correspondent regions for correcting a point of the filtered mesh.
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    Light Field Synthesis from a Single Image using Improved Wasserstein Generative Adversarial Network
    (The Eurographics Association, 2018) Ruan, Lingyan; Chen, Bin; Lam, Miu Ling; Jain, Eakta and Kosinka, Jirí
    We present a deep learning-based method to synthesize a 4D light field from a single 2D RGB image. We consider the light field synthesis problem equivalent to image super-resolution, and solve it by using the improved Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP). Experimental results demonstrate that our algorithm can predict complex occlusions and relative depths in challenging scenes. The light fields synthesized by our method has much higher signal-to-noise ratio and structural similarity than the state-of-the-art approach.
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    RIFNOM: 3D Rotation-Invariant Features on Normal Maps
    (The Eurographics Association, 2018) Nakamura, Akihiro; Miyashita, Leo; Watanabe, Yoshihiro; Ishikawa, Masatoshi; Jain, Eakta and Kosinka, Jirí
    This paper presents 3D rotation-invariant features on normal maps: RIFNOM.We assign a local coordinate system (CS) to each pixel by using neighbor normals to extract the 3D rotation-invariant features. These features can be used to perform interest point matching between normal maps. We can estimate 3D rotations between corresponding interest points by comparing local CSs. Experiments with normal maps of a rigid object showed the performance of the proposed method in estimating 3D rotations. We also applied the proposed method to a non-rigid object. By estimating 3D rotations between corresponding interest points, we successfully detected deformation of the object.