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Now showing 1 - 10 of 17
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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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    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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    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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    NeuralMLS: Geometry-Aware Control Point Deformation
    (The Eurographics Association, 2022) Shechter, Meitar; Hanocka, Rana; Metzer, Gal; Giryes, Raja; Cohen-Or, Daniel; Pelechano, Nuria; Vanderhaeghe, David
    We introduce NeuralMLS, a space-based deformation technique, guided by a set of displaced control points. We leverage the power of neural networks to inject the underlying shape geometry into the deformation parameters. The goal of our technique is to enable a realistic and intuitive shape deformation. Our method is built upon moving least-squares (MLS), since it minimizes a weighted sum of the given control point displacements. Traditionally, the influence of each control point on every point in space (i.e., the weighting function) is defined using inverse distance heuristics. In this work, we opt to learn the weighting function, by training a neural network on the control points from a single input shape, and exploit the innate smoothness of neural networks. Our geometry-aware control point deformation is agnostic to the surface representation and quality; it can be applied to point clouds or meshes, including non-manifold and disconnected surface soups. We show that our technique facilitates intuitive piecewise smooth deformations, which are well suited for manufactured objects. We show the advantages of our approach compared to existing surface and space-based deformation techniques, both quantitatively and qualitatively.
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    Simple Techniques for a Novel Human Body Pose Optimisation Using Differentiable Inverse Rendering
    (The Eurographics Association, 2022) Battogtokh, Munkhtulga; Borgo, Rita; Pelechano, Nuria; Vanderhaeghe, David
    Human body 3D reconstruction has a wide range of applications including 3D-printing, art, games, and even technical sport analysis. This is a challenging problem due to 2D ambiguity, diversity of human poses, and costs in obtaining multiple views. We propose a novel optimisation scheme that bypasses the prior bias bottleneck and the 2D-pose annotation bottleneck that we identify in single-view reconstruction, and move towards low-resource photo-realistic 3D reconstruction that directly and fully utilises the target image. Our scheme combines domain-specific method SMPLify-X and domain-agnostic inverse rendering method redner, with two simple yet powerful techniques. We demonstrate that our techniques can 1) improve the accuracy of the reconstructed body both qualitatively and quantitatively for challenging inputs, and 2) control optimisation to a selected part only. Our ideas promise extension to more difficult problems and domains even beyond human body reconstruction.
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    An Improved Triangle Encoding Scheme for Cached Tessellation
    (The Eurographics Association, 2022) Kerbl, Bernhard; Horváth, Linus; Cornel, Daniel; Wimmer, Michael; Pelechano, Nuria; Vanderhaeghe, David
    With the recent advances in real-time rendering that were achieved by embracing software rasterization, the interest in alternative solutions for other fixed-function pipeline stages rises. In this paper, we revisit a recently presented software approach for cached tessellation, which compactly encodes and stores triangles in GPU memory. While the proposed technique is both efficient and versatile, we show that the original encoding is suboptimal and provide an alternative scheme that acts as a drop-in replacement. As shown in our evaluation, the proposed modifications can yield performance gains of 40% and more.
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    Neural Motion Compression with Frequency-adaptive Fourier Feature Network
    (The Eurographics Association, 2022) Tojo, Kenji; Chen, Yifei; Umetani, Nobuyuki; Pelechano, Nuria; Vanderhaeghe, David
    We present a neural-network-based compression method to alleviate the storage cost of motion capture data. Human motions such as locomotion, often consist of periodic movements. We leverage this periodicity by applying Fourier features to a multilayered perceptron network. Our novel algorithm finds a set of Fourier feature frequencies based on the discrete cosine transformation (DCT) of motion. During training, we incrementally added a dominant frequency of the DCT to a current set of Fourier feature frequencies until a given quality threshold was satisfied. We conducted an experiment using CMU motion dataset, and the results suggest that our method achieves overall high compression ratio while maintaining its quality.