Browsing by Author "Komura, Taku"

Now showing 1 - 7 of 7
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    Binary Ostensibly-Implicit Trees for Fast Collision Detection
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Chitalu, Floyd M.; Dubach, Christophe; Komura, Taku; Panozzo, Daniele and Assarsson, Ulf
    We present a simple, efficient and low-memory technique, targeting fast construction of bounding volume hierarchies (BVH) for broad-phase collision detection. To achieve this, we devise a novel representation of BVH trees in memory. We develop a mapping of the implicit index representation to compact memory locations, based on simple bit-shifts, to then construct and evaluate bounding volume test trees (BVTT) during collision detection with real-time performance. We model the topology of the BVH tree implicitly as binary encodings which allows us to determine the nodes missing from a complete binary tree using the binary representation of the number of missing nodes. The simplicity of our technique allows for fast hierarchy construction achieving over 6x speedup over the state-of-the-art. Making use of these characteristics, we show that not only it is feasible to rebuild the BVH at every frame, but that using our technique, it is actually faster than refitting and more memory efficient.
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    Coverage Axis: Inner Point Selection for 3D Shape Skeletonization
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Dou, Zhiyang; Lin, Cheng; Xu, Rui; Yang, Lei; Xin, Shiqing; Komura, Taku; Wang, Wenping; Chaine, Raphaëlle; Kim, Min H.
    In this paper, we present a simple yet effective formulation called Coverage Axis for 3D shape skeletonization. Inspired by the set cover problem, our key idea is to cover all the surface points using as few inside medial balls as possible. This formulation inherently induces a compact and expressive approximation of the Medial Axis Transform (MAT) of a given shape. Different from previous methods that rely on local approximation error, our method allows a global consideration of the overall shape structure, leading to an efficient high-level abstraction and superior robustness to noise. Another appealing aspect of our method is its capability to handle more generalized input such as point clouds and poor-quality meshes. Extensive comparisons and evaluations demonstrate the remarkable effectiveness of our method for generating compact and expressive skeletal representation to approximate the MAT.
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    Data‐Driven Crowd Motion Control With Multi‐Touch Gestures
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Shen, Yijun; Henry, Joseph; Wang, He; Ho, Edmond S. L.; Komura, Taku; Shum, Hubert P. H.; Chen, Min and Benes, Bedrich
    Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.
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    Displacement-Correlated XFEM for Simulating Brittle Fracture
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Chitalu, Floyd M.; Miao, Qinghai; Subr, Kartic; Komura, Taku; Panozzo, Daniele and Assarsson, Ulf
    We present a remeshing-free brittle fracture simulation method under the assumption of quasi-static linear elastic fracture mechanics (LEFM). To achieve this, we devise two algorithms. First, we develop an approximate volumetric simulation, based on the extended Finite Element Method (XFEM), to initialize and propagate Lagrangian crack-fronts. We model the geometry of fracture explicitly as a surface mesh, which allows us to generate high-resolution crack surfaces that are decoupled from the resolution of the deformation mesh. Our second contribution is a mesh cutting algorithm, which produces fragments of the input mesh using the fracture surface. We do this by directly operating on the half-edge data structures of two surface meshes, which enables us to cut general surface meshes including those of concave polyhedra and meshes with abutting concave polygons. Since we avoid triangulation for cutting, the connectivity of the resulting fragments is identical to the (uncut) input mesh except at edges introduced by the cut. We evaluate our simulation and cutting algorithms and show that they outperform state-of-the-art approaches both qualitatively and quantitatively.
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    Few-shot Learning of Homogeneous Human Locomotion Styles
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Mason, Ian; Starke, Sebastian; Zhang, He; Bilen, Hakan; Komura, Taku; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Using neural networks for learning motion controllers from motion capture data is becoming popular due to the natural and smooth motions they can produce, the wide range of movements they can learn and their compactness once they are trained. Despite these advantages, these systems require large amounts of motion capture data for each new character or style of motion to be generated, and systems have to undergo lengthy retraining, and often reengineering, to get acceptable results. This can make the use of these systems impractical for animators and designers and solving this issue is an open and rather unexplored problem in computer graphics. In this paper we propose a transfer learning approach for adapting a learned neural network to characters that move in different styles from those on which the original neural network is trained. Given a pretrained character controller in the form of a Phase-Functioned Neural Network for locomotion, our system can quickly adapt the locomotion to novel styles using only a short motion clip as an example. We introduce a canonical polyadic tensor decomposition to reduce the amount of parameters required for learning from each new style, which both reduces the memory burden at runtime and facilitates learning from smaller quantities of data. We show that our system is suitable for learning stylized motions with few clips of motion data and synthesizing smooth motions in real-time.
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    Motion In-Betweening with Phase Manifolds
    (ACM Association for Computing Machinery, 2023) Starke, Paul; Starke, Sebastian; Komura, Taku; Steinicke, Frank; Wang, Huamin; Ye, Yuting; Victor Zordan
    This paper introduces a novel data-driven motion in-betweening system to reach target poses of characters by making use of phases variables learned by a Periodic Autoencoder. Our approach utilizes a mixture-of-experts neural network model, in which the phases cluster movements in both space and time with different expert weights. Each generated set of weights then produces a sequence of poses in an autoregressive manner between the current and target state of the character. In addition, to satisfy poses which are manually modified by the animators or where certain end effectors serve as constraints to be reached by the animation, a learned bi-directional control scheme is implemented to satisfy such constraints. The results demonstrate that using phases for motion in-betweening tasks sharpen the interpolated movements, and furthermore stabilizes the learning process. Moreover, using phases for motion in-betweening tasks can also synthesize more challenging movements beyond locomotion behaviors. Additionally, style control is enabled between given target keyframes. Our proposed framework can compete with popular state-of-the-art methods for motion in-betweening in terms of motion quality and generalization, especially in the existence of long transition durations. Our framework contributes to faster prototyping workflows for creating animated character sequences, which is of enormous interest for the game and film industry.
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    Random-Forest-Based Initializer for Real-time Optimization-based 3D Motion Tracking Problems
    (The Eurographics Association, 2019) Huang, Jiawei; Sugawara, Ryo; Komura, Taku; Kitamura, Yoshifumi; Kakehi, Yasuaki and Hiyama, Atsushi
    Many motion tracking systems require solving inverse problem to compute the tracking result from original sensor measurements, such as images from cameras and signals from receivers. For real-time motion tracking, such typical solutions as the Gauss-Newton method for solving their inverse problems need an initial value to optimize the cost function through iterations. A powerful initializer is crucial to generate a proper initial value for every time instance and, for achieving continuous accurate tracking without errors and rapid tracking recovery even when it is temporally interrupted. An improper initial value easily causes optimization divergence, and cannot always lead to reasonable solutions. Therefore, we propose a new initializer based on random-forest to obtain proper initial values for efficient real-time inverse problem computation. Our method trains a random-forest model with varied massive inputs and corresponding outputs and uses it as an initializer for runtime optimization. As an instance, we apply our initializer to IM3D, which is a real-time magnetic 3D motion tracking system with multiple tiny, identifiable, wireless, occlusion-free passive markers (LC coils). During run-time, a proper initial value is obtained from the initializer based on sensor measurements, and the system computes each position of the actual markers and poses by solving the inverse problem through an optimization process in real-time. We conduct four experiments to evaluate reliability and performance of the initializer. Compared with traditional or naive initializers (i.e., using a static value or random values), our results show that our proposed method provides recovery from tracking loss in a wider range of tracking space, and the entire process (initialization and optimization) can run in real-time.