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Direct Seamless Parametrization ACM Trans. Graph. (IF 5.084) Pub Date : 2021-01-18 Zohar Levi
We present a method for seamless surface parametrization. Recent popular methods first generate a cross-field, where curvature is concentrated at singular vertices. Next, in a separate step, the surface is laid out in the domain subject to derived seamlessness constraints. This decoupling of the process into two independent problems, each with its own objective, leads to suboptimal results. In contrast
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Neural Light Transport for Relighting and View Synthesis ACM Trans. Graph. (IF 5.084) Pub Date : 2021-01-18 Xiuming Zhang; Sean Fanello; Yun-Ta Tsai; Tiancheng Sun; Tianfan Xue; Rohit Pandey; Sergio Orts-Escolano; Philip Davidson; Christoph Rhemann; Paul Debevec; Jonathan T. Barron; Ravi Ramamoorthi; William T. Freeman
The light transport (LT) of a scene describes how it appears under different lighting conditions from different viewing directions, and complete knowledge of a scene’s LT enables the synthesis of novel views under arbitrary lighting. In this article, we focus on image-based LT acquisition, primarily for human bodies within a light stage setup. We propose a semi-parametric approach for learning a neural
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A Conversational Agent Framework with Multi-modal Personality Expression ACM Trans. Graph. (IF 5.084) Pub Date : 2021-01-06 Sinan Sonlu; Uğur Güdükbay; Funda Durupinar
Consistently exhibited personalities are crucial elements of realistic, engaging, and behavior-rich conversational virtual agents. Both nonverbal and verbal cues help convey these agents’ unseen psychological states, contributing to our effective communication with them. We introduce a comprehensive framework to design conversational agents that express personality through non-verbal behaviors like
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Optimized Refinement for Spatially Adaptive SPH ACM Trans. Graph. (IF 5.084) Pub Date : 2021-01-06 Rene Winchenbach; Andreas Kolb
In this article, we propose an improved refinement process for the simulation of incompressible low-viscosity turbulent flows using Smoothed Particle Hydrodynamics, under adaptive volume ratios of up to 1 : 1, 000, 000. We derive a discretized objective function, which allows us to generate ideal refinement patterns for any kernel function and any number of particles a priori without requiring intuitive
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A Safe and Fast Repulsion Method for GPU-based Cloth Self Collisions ACM Trans. Graph. (IF 5.084) Pub Date : 2020-12-24 Longhua Wu; Botao Wu; Yin Yang; Huamin Wang
Cloth dynamics and collision handling are the two most challenging topics in cloth simulation. While researchers have substantially improved the performances of cloth dynamics solvers recently, their success in fast collision detection and handling is rather limited. In this article, we focus our research on the safety, efficiency, and realism of the repulsion-based collision handling approach, which
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Dynamic Upsampling of Smoke through Dictionary-based Learning ACM Trans. Graph. (IF 5.084) Pub Date : 2020-09-18 Kai Bai; Wei Li; Mathieu Desbrun; Xiaopei Liu
Simulating turbulent smoke flows with fine details is computationally intensive. For iterative editing or simply faster generation, efficiently upsampling a low-resolution numerical simulation is an attractive alternative. We propose a novel learning approach to the dynamic upsampling of smoke flows based on a training set of flows at coarse and fine resolutions. Our multiscale neural network turns
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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