Browsing by Author "Fratarcangeli, Marco"

Now showing 1 - 6 of 6
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    Fast Nonlinear Least Squares Optimization of Large-Scale Semi-Sparse Problems
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Fratarcangeli, Marco; Bradley, Derek; Gruber, Aurel; Zoss, Gaspard; Beeler, Thabo; Panozzo, Daniele and Assarsson, Ulf
    Many problems in computer graphics and vision can be formulated as a nonlinear least squares optimization problem, for which numerous off-the-shelf solvers are readily available. Depending on the structure of the problem, however, existing solvers may be more or less suitable, and in some cases the solution comes at the cost of lengthy convergence times. One such case is semi-sparse optimization problems, emerging for example in localized facial performance reconstruction, where the nonlinear least squares problem can be composed of hundreds of thousands of cost functions, each one involving many of the optimization parameters. While such problems can be solved with existing solvers, the computation time can severely hinder the applicability of these methods. We introduce a novel iterative solver for nonlinear least squares optimization of large-scale semi-sparse problems. We use the nonlinear Levenberg-Marquardt method to locally linearize the problem in parallel, based on its firstorder approximation. Then, we decompose the linear problem in small blocks, using the local Schur complement, leading to a more compact linear system without loss of information. The resulting system is dense but its size is small enough to be solved using a parallel direct method in a short amount of time. The main benefit we get by using such an approach is that the overall optimization process is entirely parallel and scalable, making it suitable to be mapped onto graphics hardware (GPU). By using our minimizer, results are obtained up to one order of magnitude faster than other existing solvers, without sacrificing the generality and the accuracy of the model. We provide a detailed analysis of our approach and validate our results with the application of performance-based facial capture using a recently-proposed anatomical local face deformation model.
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    Interactive Assembly and Animation of 3D Digital Garments
    (The Eurographics Association, 2020) Nylén, Oskar; Pall, Pontus; Ishiwaka, Yuko; Suda, Kazuto; Fratarcangeli, Marco; Wilkie, Alexander and Banterle, Francesco
    We present a novel real-time tool for sewing together 2D patterns, enabling quick assembly of visually plausible, interactively animated garments for virtual characters. The process is assisted by ad-hoc visual hints and allows designers to import 2D patterns from any CAD-tool, connect them using seams around a 3D character with any body type, and assess the overall quality during the character animation. The cloth is numerically simulated including robust modeling of contact of the cloth with itself and with the character's body. Overall, our tool allows for fast prototyping of virtual garments, achieving immediate feedback on their behaviour and visual quality on an animated character, in effect speeding up the content production pipeline for visual effects applications involving clothed characters.
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    Interactive Sculpting of Digital Faces Using an Anatomical Modeling Paradigm
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Gruber, Aurel; Fratarcangeli, Marco; Zoss, Gaspard; Cattaneo, Roman; Beeler, Thabo; Gross, Markus; Bradley, Derek; Jacobson, Alec and Huang, Qixing
    Digitally sculpting 3D human faces is a very challenging task. It typically requires either 1) highly-skilled artists using complex software packages for high quality results, or 2) highly-constrained simple interfaces for consumer-level avatar creation, such as in game engines. We propose a novel interactive method for the creation of digital faces that is simple and intuitive to use, even for novice users, while consistently producing plausible 3D face geometry, and allowing editing freedom beyond traditional video game avatar creation. At the core of our system lies a specialized anatomical local face model (ALM), which is constructed from a dataset of several hundred 3D face scans. User edits are propagated to constraints for an optimization of our data-driven ALM model, ensuring the resulting face remains plausible even for simple edits like clicking and dragging surface points. We show how several natural interaction methods can be implemented in our framework, including direct control of the surface, indirect control of semantic features like age, ethnicity, gender, and BMI, as well as indirect control through manipulating the underlying bony structures. The result is a simple new method for creating digital human faces, for artists and novice users alike. Our method is attractive for low-budget VFX and animation productions, and our anatomical modeling paradigm can complement traditional game engine avatar design packages.
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    Multi‐Variate Gaussian‐Based Inverse Kinematics
    (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Huang, Jing; Wang, Qi; Fratarcangeli, Marco; Yan, Ke; Pelachaud, Catherine; Chen, Min and Zhang, Hao (Richard)
    Inverse kinematics (IK) equations are usually solved through approximated linearizations or heuristics. These methods lead to character animations that are unnatural looking or unstable because they do not consider both the motion coherence and limits of human joints. In this paper, we present a method based on the formulation of multi‐variate Gaussian distribution models (MGDMs), which precisely specify the soft joint constraints of a kinematic skeleton. Each distribution model is described by a covariance matrix and a mean vector representing both the joint limits and the coherence of motion of different limbs. The MGDMs are automatically learned from the motion capture data in a fast and unsupervised process. When the character is animated or posed, a Gaussian process synthesizes a new MGDM for each different vector of target positions, and the corresponding objective function is solved with Jacobian‐based IK. This makes our method practical to use and easy to insert into pre‐existing animation pipelines. Compared with previous works, our method is more stable and more precise, while also satisfying the anatomical constraints of human limbs. Our method leads to natural and realistic results without sacrificing real‐time performance.Inverse kinematics (IK) equations are usually solved through approximated linearizations or heuristics. These methods lead to character animations that are unnatural looking or unstable because they do not consider both the motion coherence and limits of human joints. In this paper, we present a method based on the formulation of multi‐variate Gaussian distribution models (MGDMs), which precisely specify the soft joint constraints of a kinematic skeleton. Each distribution model is described by a covariance matrix and a mean vector representing both the joint limits and the coherence of motion of different limbs.
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    Parallel Multigrid for Nonlinear Cloth Simulation
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Wang, Zhendong; Wu, Longhua; Fratarcangeli, Marco; Tang, Min; Wang, Huamin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Accurate high-resolution simulation of cloth is a highly desired computational tool in graphics applications. As singleresolution simulation starts to reach the limit of computational power, we believe the future of cloth simulation is in multi-resolution simulation. In this paper, we explore nonlinearity, adaptive smoothing, and parallelization under a full multigrid (FMG) framework. The foundation of this research is a novel nonlinear FMG method for unstructured meshes. To introduce nonlinearity into FMG, we propose to formulate the smoothing process at each resolution level as the computation of a search direction for the original high-resolution nonlinear optimization problem. We prove that our nonlinear FMG is guaranteed to converge under various conditions and we investigate the improvements to its performance. We present an adaptive smoother which is used to reduce the computational cost in the regions with low residuals already. Compared to normal iterative solvers, our nonlinear FMG method provides faster convergence and better performance for both Newton's method and Projective Dynamics. Our experiment shows our method is efficient, accurate, stable against large time steps, and friendly with GPU parallelization. The performance of the method has a good scalability to the mesh resolution, and the method has good potential to be combined with multi-resolution collision handling for real-time simulation in the future.
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    Task-based Colormap Design Supporting Visual Comprehension in Process Tomography
    (The Eurographics Association, 2020) Zhang, Yuchong; Fjeld, Morten; Said, Alan; Fratarcangeli, Marco; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
    Color coding is a fundamental technique for mapping data to visual representations, allowing people to carry out comprehension-based tasks. Process tomography is a rapidly developing non-invasive imaging technique used in various fields of science due to its effective flow monitoring and data acquisition [KLS*19]. To study how well colormaps can support visual comprehension of tomographic data, we conduct a feasibility evaluation of 11 widely-used color schemes. We employ the same segmentation tasks characterized by Microwave Tomography (MWT) on each individual chosen colormap, and then conduct a quantitative assessment of those schemes. Based on the insight gained, we conclude that autumn, viridis, and parula colormaps yield the best segmentation results. According to our findings, we propose a colormap design guideline for practitioners and researchers in the field of process tomography.