Browsing by Author "Umetani, Nobuyuki"

Now showing 1 - 9 of 9
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    Accurate Boundary Condition for Moving Least Squares Material Point Method using Augmented Grid Points
    (The Eurographics Association, 2024) Toyota, Riku; Umetani, Nobuyuki; Hu, Ruizhen; Charalambous, Panayiotis
    This paper introduces an accurate boundary-handling method for the moving least squares (MLS) material point method (MPM), which is a popular scheme for robustly simulating deformable objects and fluids using a hybrid of particle and grid representations coupled via MLS interpolation. Despite its versatility with different materials, traditional MPM suffers from undesirable artifacts around wall boundaries, for example, particles pass through the walls and accumulate. To address these issues, we present a technique inspired by a line handler for MLS-based image manipulation. Specifically, we augment the grid by adding points along the wall boundary to numerically compute the integration of the MLS weight. These additional points act as background grid points, improving the accuracy of the MLS interpolation around the boundary, albeit with a marginal increase in computational cost. In particular, our technique makes the velocity perpendicular to the wall nearly zero, preventing particles from passing through the wall. We compare the boundary behavior of 2D simulation against that of naïve approach.
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    Data-driven Garment Pattern Estimation from 3D Geometries
    (The Eurographics Association, 2021) Goto, Chihiro; Umetani, Nobuyuki; Theisel, Holger and Wimmer, Michael
    Three-dimensional scanning technology recently becomes widely available to the public. However, it is difficult to simulate clothing deformation from the scanned people because scanned data lacks information required for the clothing simulation. In this paper, we present a technique to estimate clothing patterns from a scanned person in cloth. Our technique uses image-based deep learning to estimate the type of pattern on the projected image. The key contribution is converting image-based inference into three-dimensional clothing pattern estimation. We evaluate our technique by applying our technique to an actual scan.
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    Fast Grayscale Morphology for Circular Window
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Moroto, Yuji; Umetani, Nobuyuki; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Morphological operations are among the most popular classic image filters. The filter assumes the maximum or minimum value within a window and is often used for light object thickening and thinning operations, which are important components of various workflows, such as object recognition and stylization. Circular windows are preferred over rectangular windows for obtaining isotropic filter results. However, the existing efficient algorithms focus on rectangular or binary input images. Efficient morphological operations with circular windows for grayscale images remain challenging. In this study, we present a fast grayscale morphology heuristic computation algorithm that decomposes circular windows using the convex hull of circles. We significantly accelerate traditional methods based on Minkowski addition by introducing new decomposition rules specialized for circular windows. As our morphological operation using a convex hull can be computed independently for each pixel, the algorithm is efficient for modern multithreaded hardware.
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    Fast Polygonal Splatting using Directional Kernel Difference
    (The Eurographics Association, 2021) Moroto, Yuji; Hachisuka, Toshiya; Umetani, Nobuyuki; Bousseau, Adrien and McGuire, Morgan
    Depth-of-field (DoF) filtering is an important image-processing task for producing blurred images similar to those obtained with a large aperture camera lens. DoF filtering applies an image convolution with a spatially varying kernel and is thus computationally expensive, even on modern computational hardware. In this paper, we introduce an approach for fast and accurate DoF filtering for polygonal kernels, where the value is constant inside the kernel. Our approach is an extension of the existing approach based on discrete differenced kernels. The performance gain here hinges upon the fact that kernels typically become sparse (i.e., mostly zero) when taking the difference. We extended the existing approach to conventional axis-aligned differences to non-axis-aligned differences. The key insight is that taking such differences along the directions of the edges makes polygonal kernels significantly sparser than just taking the difference along the axis-aligned directions, as in existing studies. Compared to a naive image convolution, we achieve an order of magnitude speedup, allowing a real-time application of polygonal kernels even on high-resolution images.
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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.
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    Neural Sequence Transformation
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Mukherjee, Sabyasachi; Mukherjee, Sayan; Hua, Binh-Son; Umetani, Nobuyuki; Meister, Daniel; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
    Monte Carlo integration is a technique for numerically estimating a definite integral by stochastically sampling its integrand. These samples can be averaged to make an improved estimate, and the progressive estimates form a sequence that converges to the integral value on the limit. Unfortunately, the sequence of Monte Carlo estimates converges at a rate of O(pn), where n denotes the sample count, effectively slowing down as more samples are drawn. To overcome this, we can apply sequence transformation, which transforms one converging sequence into another with the goal of accelerating the rate of convergence. However, analytically finding such a transformation for Monte Carlo estimates can be challenging, due to both the stochastic nature of the sequence, and the complexity of the integrand. In this paper, we propose to leverage neural networks to learn sequence transformations that improve the convergence of the progressive estimates of Monte Carlo integration. We demonstrate the effectiveness of our method on several canonical 1D integration problems as well as applications in light transport simulation.
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    Pacific Graphics 2022 - CGF 41-7: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
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    Recolorable Posterization of Volumetric Radiance Fields Using Visibility-Weighted Palette Extraction
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Tojo, Kenji; Umetani, Nobuyuki; Ghosh, Abhijeet; Wei, Li-Yi
    Volumetric radiance fields have recently gained significant attention as promising representations of photorealistic scene reconstruction. However, the non-photorealistic rendering of such a representation has barely been explored. In this study, we investigate the artistic posterization of the volumetric radiance fields. We extend the recent palette-based image-editing framework, which naturally introduces intuitive color manipulation of the posterized results, into the radiance field. Our major challenge is applying stylization effects coherently across different views. Based on the observation that computing a palette frame-by-frame can produce flickering, we propose pre-computing a single palette from the volumetric radiance field covering its entire visible color. We present a method based on volumetric visibility to sample visible colors from the radiance field while avoiding occluded and noisy regions. We demonstrate our workflow by applying it to pre-trained volumetric radiance fields with various stylization effects. We also show that our approach can produce more coherent and robust stylization effects than baseline methods that compute a palette on each rendered view.
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    Two-Way Coupling of Skinning Transformations and Position Based Dynamics
    (ACM Association for Computing Machinery, 2023) Wu, Yuhan; Umetani, Nobuyuki; Wang, Huamin; Ye, Yuting; Victor Zordan
    Skinning transformations enable digital characters to be animated with minimal user input. Physics simulations can improve the detailed dynamic movement of an animated character; however, such details are typically added in the post-processing stage after the overall animation is specified.We propose a novel interactive framework that unifies skinning transformations and kinematic simulations using position-based dynamics (PBD). Our framework allows an arbitrarily skinned character to be partially manipulated by the user, and a kinematic physics solver automatically complements the behavior of the entire character. This is achieved by introducing new steps into the PBD algorithm: (i) lightweight optimization to identify the skinning transformations, which is similar to inverse kinematics, and (ii) a position-based constraint to restrict the PBD solver to the complementary subspace of the skinning deformation. Our method combines the best of the two methods: the controllability and shape preservation of the skinning transformation, and the efficiency, simplicity, and unconditional stability of the PBD solver. Our interface allows novices to create vibrant animations without tedious editing.