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  • DeepMag: Source-Specific Change Magnification Using Gradient Ascent
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-09-09
    Weixuan Chen; Daniel McDuff

    Many important physical phenomena involve subtle signals that are difficult to observe with the unaided eye, yet visualizing them can be very informative. Current motion magnification techniques can reveal these small temporal variations in video, but require precise prior knowledge about the target signal, and cannot deal with interference motions at a similar frequency. We present DeepMag, an end-to-end

  • MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-09-04
    Mingyi Shi; Kfir Aberman; Andreas Aristidou; Taku Komura; Dani Lischinski; Daniel Cohen-Or; Baoquan Chen

    We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from a monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used

  • SIERE: A Hybrid Semi-Implicit Exponential Integrator for Efficiently Simulating Stiff Deformable Objects
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-08-26
    Yu Ju (Edwin) Chen; Seung Heon Sheen; Uri M. Ascher; Dinesh K. Pai

    Physics-based simulation methods for deformable objects suffer limitations due to the conflicting requirements that are placed on them. The work horse semi-implicit (SI) backward Euler method is very stable and inexpensive, but it is also a blunt instrument. It applies heavy damping, which depends on the timestep, to all solution modes and not just to high-frequency ones. As such, it makes simulations

  • A Class ofC2Interpolating Splines
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-08-21
    Cem Yuksel

    We present a class of non-polynomial parametric splines that interpolate the given control points and show that some curve types in this class have a set of highly desirable properties that were not previously demonstrated for interpolating curves before. In particular, the formulation of this class guarantees that the resulting curves have C2 continuity everywhere and local support, such that only

  • Noise-Resilient Reconstruction of Panoramas and 3D Scenes Using Robot-Mounted Unsynchronized Commodity RGB-D Cameras
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-07-01
    Sheng Yang; Beichen Li; Yan-Pei Cao; Hongbo Fu; Yu-Kun Lai; Leif Kobbelt; Shi-Min Hu

    We present a two-stage approach to first constructing 3D panoramas and then stitching them for noise-resilient reconstruction of large-scale indoor scenes. Our approach requires multiple unsynchronized RGB-D cameras, mounted on a robot platform, which can perform in-place rotations at different locations in a scene. Such cameras rotate on a common (but unknown) axis, which provides a novel perspective

  • Reinforcement of General Shell Structures
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-06-09
    Francisca Gil-Ureta; Nico Pietroni; Denis Zorin

    We introduce an efficient method for designing shell reinforcements of minimal weight. Inspired by classical Michell trusses, we create a reinforcement layout whose members are aligned with optimal stress directions, then optimize their shape minimizing the volume while keeping stresses bounded. We exploit two predominant techniques for reinforcing shells: adding ribs aligned with stress directions

  • Inverse Procedural Modeling of Branching Structures by Inferring L-Systems
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-06-15
    Jianwei Guo; Haiyong Jiang; Bedrich Benes; Oliver Deussen; Xiaopeng Zhang; Dani Lischinski; Hui Huang

    We introduce an inverse procedural modeling approach that learns L-system representations of pixel images with branching structures. Our fully automatic model generates a compact set of textual rewriting rules that describe the input. We use deep learning to discover atomic structures such as line segments or branchings. Orientation and scaling of these structures are determined and the detected structures

  • Kinetic Shape Reconstruction
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-06-15
    Jean-Philippe Bauchet; Florent Lafarge

    Converting point clouds into concise polygonal meshes in an automated manner is an enduring problem in computer graphics. Prior works, which typically operate by assembling planar shapes detected from input points, largely overlooked the scalability issue of processing a large number of shapes. As a result, they tend to produce overly simplified meshes with assembling approaches that can hardly digest

  • 3D Morphable Face Models—Past, Present, and Future
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-06-09
    Bernhard Egger; William A. P. Smith; Ayush Tewari; Stefanie Wuhrer; Michael Zollhoefer; Thabo Beeler; Florian Bernard; Timo Bolkart; Adam Kortylewski; Sami Romdhani; Christian Theobalt; Volker Blanz; Thomas Vetter

    In this article, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely, capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions

  • Capturing Subjective First-Person View Shots with Drones for Automated Cinematography
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-08-10
    Amirsaman Ashtari; Stefan Stevšić; Tobias Nägeli; Jean-Charles Bazin; Otmar Hilliges

    We propose an approach to capture subjective first-person view (FPV) videos by drones for automated cinematography. FPV shots are intentionally not smooth to increase the level of immersion for the audience, and are usually captured by a walking camera operator holding traditional camera equipment. Our goal is to automatically control a drone in such a way that it imitates the motion dynamics of a

  • Computational p-Willmore Flow with Conformal Penalty
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-08-10
    Anthony Gruber; Eugenio Aulisa

    The unsigned p-Willmore functional introduced in the work of Mondino [2011] generalizes important geometric functionals, which measure the area and Willmore energy of immersed surfaces. Presently, techniques from the work of Dziuk [2008] are adapted to compute the first variation of this functional as a weak-form system of equations, which are subsequently used to develop a model for the p-Willmore

  • Example-Based Microstructure Rendering with Constant Storage
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-08-10
    Beibei Wang; Miloš Hašan; Nicolas Holzschuch; Ling-Qi Yan

    Rendering glinty details from specular microstructure enhances the level of realism, but previous methods require heavy storage for the high-resolution height field or normal map and associated acceleration structures. In this article, we aim at dynamically generating theoretically infinite microstructure, preventing obvious tiling artifacts, while achieving constant storage cost. Unlike traditional

  • Enhanced Interactive 360° Viewing via Automatic Guidance
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-05-31
    Seunghoon Cha; Jungjin Lee; Seunghwa Jeong; Younghui Kim; Junyong Noh

    We present a new interactive playback method to enhance 360° viewing experiences. Our method automatically rotates the virtual camera of a 360° panoramic video (360° video) player during interactive viewing to guide the viewer through the most important regions of the video. With this method, the viewer can watch a 360° video with minimum efforts to find important events in a scene both in interactive

  • Unsupervised Detection of Distinctive Regions on 3D Shapes
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-05-31
    Xianzhi Li; Lequan Yu; Chi-Wing Fu; Daniel Cohen-Or; Pheng-Ann Heng

    This article presents a novel approach to learn and detect distinctive regions on 3D shapes. Unlike previous works, which require labeled data, our method is unsupervised. We conduct the analysis on point sets sampled from 3D shapes, then formulate and train a deep neural network for an unsupervised shape clustering task to learn local and global features for distinguishing shapes with respect to a

  • VoroCrust: Voronoi Meshing Without Clipping.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2020-05-01
    Ahmed Abdelkader,Chandrajit L Bajaj,Mohamed S Ebeida,Ahmed H Mahmoud,Scott A Mitchell,John D Owens,Ahmad A Rushdi

    Polyhedral meshes are increasingly becoming an attractive option with particular advantages over traditional meshes for certain applications. What has been missing is a robust polyhedral meshing algorithm that can handle broad classes of domains exhibiting arbitrary curved boundaries and sharp features. In addition, the power of primal-dual mesh pairs, exemplified by Voronoi-Delaunay meshes, has been

  • Tensor Maps for Synchronizing Heterogeneous Shape Collections.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2019-07-26
    Qixing Huang,Zhenxiao Liang,Haoyun Wang,Simiao Zuo,Chandrajit Bajaj

    Establishing high-quality correspondence maps between geometric shapes has been shown to be the fundamental problem in managing geometric shape collections. Prior work has focused on computing efficient maps between pairs of shapes, and has shown a quantifiable benefit of joint map synchronization, where a collection of shapes are used to improve (denoise) the pairwise maps for consistency and correctness

  • Fast and Exact Continuous Collision Detection with Bernstein Sign Classification.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2015-01-09
    Min Tang,Ruofeng Tong,Zhendong Wang,Dinesh Manocha

    We present fast algorithms to perform accurate CCD queries between triangulated models. Our formulation uses properties of the Bernstein basis and Bézier curves and reduces the problem to evaluating signs of polynomials. We present a geometrically exact CCD algorithm based on the exact geometric computation paradigm to perform reliable Boolean collision queries. Our algorithm is more than an order

  • Perception of Perspective Distortions in Image-Based Rendering.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2013-11-26
    Peter Vangorp,Christian Richardt,Emily A Cooper,Gaurav Chaurasia,Martin S Banks,George Drettakis

    Image-based rendering (IBR) creates realistic images by enriching simple geometries with photographs, e.g., mapping the photograph of a building façade onto a plane. However, as soon as the viewer moves away from the correct viewpoint, the image in the retina becomes distorted, sometimes leading to gross misperceptions of the original geometry. Two hypotheses from vision science state how viewers perceive

  • Optimizing Locomotion Controllers Using Biologically-Based Actuators and Objectives.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2012-07-01
    Jack M Wang,Samuel R Hamner,Scott L Delp,Vladlen Koltun

    We present a technique for automatically synthesizing walking and running controllers for physically-simulated 3D humanoid characters. The sagittal hip, knee, and ankle degrees-of-freedom are actuated using a set of eight Hill-type musculotendon models in each leg, with biologically-motivated control laws. The parameters of these control laws are set by an optimization procedure that satisfies a number

  • Using Blur to Affect Perceived Distance and Size.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2010-03-01
    Robert T Held,Emily A Cooper,James F O'Brien,Martin S Banks

    We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image's contents. From the model, we develop a semiautomated algorithm that applies blur to a sharply rendered

  • Optimizing Cubature for Efficient Integration of Subspace Deformations.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2009-12-04
    Steven S An,Theodore Kim,Doug L James

    We propose an efficient scheme for evaluating nonlinear subspace forces (and Jacobians) associated with subspace deformations. The core problem we address is efficient integration of the subspace force density over the 3D spatial domain. Similar to Gaussian quadrature schemes that efficiently integrate functions that lie in particular polynomial subspaces, we propose cubature schemes (multi-dimensional

  • Discovering Structural Regularity in 3D Geometry.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2008-08-01
    Mark Pauly,Niloy J Mitra,Johannes Wallner,Helmut Pottmann,Leonidas J Guibas

    We introduce a computational framework for discovering regular or repeated geometric structures in 3D shapes. We describe and classify possible regular structures and present an effective algorithm for detecting such repeated geometric patterns in point- or mesh-based models. Our method assumes no prior knowledge of the geometry or spatial location of the individual elements that define the pattern

  • Misperceptions in Stereoscopic Displays: A Vision Science Perspective.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2008-01-01
    Robert T Held,Martin S Banks

    3d shape and scene layout are often misperceived when viewing stereoscopic displays. For example, viewing from the wrong distance alters an object's perceived size and shape. It is crucial to understand the causes of such misperceptions so one can determine the best approaches for minimizing them. The standard model of misperception is geometric. The retinal images are calculated by projecting from

  • Meshless Animation of Fracturing Solids.
    ACM Trans. Graph. (IF 5.084) Pub Date : 2005-07-01
    Mark Pauly,Richard Keiser,Bart Adams,Philip Dutré,Markus Gross,Leonidas J Guibas

    We present a new meshless animation framework for elastic and plastic materials that fracture. Central to our method is a highly dynamic surface and volume sampling method that supports arbitrary crack initiation, propagation, and termination, while avoiding many of the stability problems of traditional mesh-based techniques. We explicitly model advancing crack fronts and associated fracture surfaces

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