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Now showing 1 - 10 of 22
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    Learning Generic Local Shape Properties for Adaptive Super-Sampling
    (The Eurographics Association, 2022) Reinbold, Christian; Westermann, Rüdiger; Pelechano, Nuria; Vanderhaeghe, David
    We propose a novel encoder/decoder-based neural network architecture that learns view-dependent shape and appearance of geometry represented by voxel representations. Since the network is trained on local geometry patches, it generalizes to arbitrary models. A geometry model is first encoded into a sparse voxel octree of features learned by a network, and this model representation can then be decoded by another network in-turn for the intended task. We utilize the network for adaptive supersampling in ray-tracing, to predict super-sampling patterns when seeing coarse-scale geometry. We discuss and evaluate the proposed network design, and demonstrate that the decoder network is compact and can be integrated seamlessly into on-chip ray-tracing kernels. We compare the results to previous screen-space super-sampling strategies as well as non-network-based world-space approaches.
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    Graph Partitioning Algorithms for Rigid Body Simulations
    (The Eurographics Association, 2022) Liu, Yinchu; Andrews, Sheldon; Pelechano, Nuria; Vanderhaeghe, David
    We propose several graph partitioning algorithms for improving the performance of rigid body simulations. The algorithms operate on the graph formed by rigid bodies (nodes) and constraints (edges), producing non-overlapping and contiguous sub-systems that can be simulated in parallel by a domain decomposition technique. We demonstrate that certain partitioning algorithms reduce the computational time of the solver, and graph refinement techniques that reduce coupling between sub-systems, such as the Kernighan-Lin and Fiduccia-Mattheyses algorithms, give additional performance improvements.
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    Interactive Facial Expression Editing with Non-linear Blendshape Interpolation
    (The Eurographics Association, 2022) Roh, Ji Hyun; Kim, Seong Uk; Jang, Hanyoung; Seol, Yeongho; Kim, Jongmin; Pelechano, Nuria; Vanderhaeghe, David
    The ability to manipulate facial animations interactively is vital for enhancing the productivity and quality of character animation. In this paper, we present a novel interactive facial animation editing system that can express the naturalness of non-linear facial movements in real-time. The proposed system is based on a fully automatic algorithm that maintains all positional constraints while deforming the facial mesh as realistic as possible. Our method is based on direct manipulation with non-linear blendshape interpolation. We formulate the facial animation editing as a two-step quadratic minimization and solve it efficiently. From our results, the proposed method produces the desired and realistic facial animation better compared to existing mesh deformation methods, which are mainly based on linear combination and optimization.
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    EUROGRAPHICS 2022: Short Papers Frontmatter
    (The Eurographics Association, 2022) Pelechano, Nuria; Vanderhaeghe, David; Pelechano, Nuria; Vanderhaeghe, David
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    Scene Synthesis with Automated Generation of Textual Descriptions
    (The Eurographics Association, 2022) Müller-Huschke, Julian; Ritter, Marcel; Harders, Matthias; Pelechano, Nuria; Vanderhaeghe, David
    Most current research on automatically captioning and describing scenes with spatial content focuses on images. We outline that generating descriptive text for a synthesized 3D scene can be achieved via a suitable intermediate representation employed in the synthesis algorithm. As an example, we synthesize scenes of medieval village settings, and generate their descriptions. Our system employs graph grammars, Markov Chain Monte Carlo optimization, and a natural language generation pipeline. Randomly placed objects are evaluated and optimized by a cost function capturing neighborhood relations, path layouts, and collisions. Further, in a pilot study we assess the performance of our framework by comparing the generated descriptions to others provided by human subjects. While the latter were often short and low-effort, the highest-rated ones clearly outperform our generated ones. Nevertheless, the average of all collected human descriptions was indeed rated by the study participants as being less accurate than the automated ones.
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    Resolving Non-Manifoldness on Meshes from Dual Marching Cubes
    (The Eurographics Association, 2022) Zint, Daniel; Grosso, Roberto; Gürtler, Philipp; Pelechano, Nuria; Vanderhaeghe, David
    There are several methods that reconstruct surfaces from volume data by generating triangle or quad meshes on the dual of the uniform grid. Those methods often provide meshes with better quality than the famous marching cubes. However, they have a common issue: the meshes are not guaranteed to be manifold. We address this issue by presenting a post-processing routine that resolves all non-manifold edges with local refinement. New vertices are positioned on the trilinear interpolant. We verify our method on a wide range of data sets and show that we are capable of resolving all non-manifold issues.
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    Procedural Bridges-and-pillars Support Generation
    (The Eurographics Association, 2022) Freire, Marco; Hornus, Samuel; Perchy, Salim; Lefebvre, Sylvain; Pelechano, Nuria; Vanderhaeghe, David
    Additive manufacturing requires support structures to fabricate parts with overhangs. In this paper, we revisit a known support structure based on bridges-and-pillars (see Figure 1). The support structures are made of vertical pillars supporting horizontal bridges. Their scaffolding structure makes them stable and reliable to print. However, the algorithm heuristic search does not scale well and is prone to produce contacts with the parts, leaving scars after removal. We propose a novel algorithm for this type of supports, focusing on avoiding unnecessary contacts with the part as much as possible. Our approach builds upon example-based model synthesis to enable early detection of collision-free passages as well as non-reachable regions.
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    Transparent Rendering and Slicing of Integral Surfaces Using Per-primitive Interval Arithmetic
    (The Eurographics Association, 2022) Aydinlilar, Melike; Zanni, Cédric; Pelechano, Nuria; Vanderhaeghe, David
    We present a method for efficient incorporation of integral surfaces within existing robust processing methods such as interval arithmetic and segment-tracing. We based our approach on high-level knowledge of the field function of the primitives. We show application to slicing and transparent rendering of integral surfaces based on interval arithmetic.
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    Stochastic Light Culling for Single Scattering in Participating Media
    (The Eurographics Association, 2022) Fujieda, Shin; Tokuyoshi, Yusuke; Harada, Takahiro; Pelechano, Nuria; Vanderhaeghe, David
    We introduce a simple but efficient method to compute single scattering from point and arbitrarily shaped area light sources in participating media. Our method extends the stochastic light culling method to volume rendering by considering the intersection of a ray and spherical bounds of light influence ranges. For primary rays, this allows simple computation of the lighting in participating media without hierarchical data structures such as a light tree. First, we show how to combine equiangular sampling with the proposed light culling method in a simple case of point lights. We then apply it to arbitrarily shaped area lights by considering virtual point lights on the surface of area lights. Using our method, we are able to improve the rendering quality for scenes with many lights without tree construction and traversal.
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    AvatarGo: Plug and Play self-avatars for VR
    (The Eurographics Association, 2022) Ponton, Jose Luis; Monclús, Eva; Pelechano, Nuria; Pelechano, Nuria; Vanderhaeghe, David
    The use of self-avatars in a VR application can enhance presence and embodiment which leads to a better user experience. In collaborative VR it also facilitates non-verbal communication. Currently it is possible to track a few body parts with cheap trackers and then apply IK methods to animate a character. However, the correspondence between trackers and avatar joints is typically fixed ad-hoc, which is enough to animate the avatar, but causes noticeable mismatches between the user's body pose and the avatar. In this paper we present a fast and easy to set up system to compute exact offset values, unique for each user, which leads to improvements in avatar movement. Our user study shows that the Sense of Embodiment increased significantly when using exact offsets as opposed to fixed ones. We also allowed the users to see a semitransparent avatar overlaid with their real body to objectively evaluate the quality of the avatar movement with our technique.