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Stable Neo-Hookean Flesh Simulation

What Are Optimal Coding Functions for Time-of-Flight Imaging?

Pressure Boundaries for Implicit Incompressible SPH

Discrete Geodesic Nets for Modeling Developable Surfaces

Precomputed Panel Solver for Aerodynamics Simulation

Modeling n-Symmetry Vector Fields using Higher-Order Energies

Integrating Clipped Spherical Harmonics Expansions

Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines

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About TOG

ACM TOG is the foremost peer-reviewed journal in the area of computer graphics. 

Recent impact factor calculations from Thomson Reuters give ACM TOG an impact factor of 4.096 and an Eigenfactor Score of 0.029, giving it the top ranking among the 104 journals in the Computer Science: Software Engineering category. 

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Forthcoming Articles
Spline Interface for Interactive Skinning Weight Editing

Despite the recent advances in automatic methods for computing skinning weights, manual intervention is still indispensable to produce high quality character deformation. However, current modeling software does not provide efficient tools for the manual definition of skinning weights. A widely used paint-based interface gives a users high degrees of freedom, but at the expense of significant efforts and time. This paper presents a novel interface for editing skinning weights based on splines, which represent the isolines of skinning weights on a mesh. When a user drags a small number of spline anchor points, our method updates the shape of the isolines and smoothly interpolates or propagates the weights while respecting the given iso-value on the spline. We introduce several techniques to enable the interface to run in real-time, and propose a particular combination of functions that generates appropriate skinning weight distribution over the surface given splines. Users can create skinning weight from the scratch by using our method. In addition, we present the spline and the gradient fitting methods that closely approximate an initial weight made by an automatic method or a paint interface, so that a user can modify the given weight with our spline interface.We show the effectiveness of our spline-based interface through a number of test cases.

FaceVR: Real-Time Gaze-Aware Facial Reenactment in Virtual Reality

We propose FaceVR, a novel image-based method that enables video teleconferencing in VR based on self-reenactment. State-of-the-art face tracking methods in the VR context are focused on the animation of rigged 3d avatars [Li et al. 2015; Olszewski et al. 2016]. While they achieve good tracking performance the results look cartoonish and not real. In contrast to these model-based approaches, FaceVR enables VR teleconferencing using an image-based technique that results in nearly photo-realistic outputs. The key component of FaceVR is a robust algorithm to perform real-time facial motion capture of an actor who is wearing a head-mounted display (HMD), as well as a new data-driven approach for eye tracking from monocular videos. Based on reenactment of a prerecorded stereo video of the person without the HMD, FaceVR incorporates photo-realistic re-rendering in real time, thus allowing artificial modifications of face and eye appearances. For instance, we can alter facial expressions or change gaze directions in the prerecorded target video. In a live setup, we apply these newly-introduced algorithmic components.

Automatic Machine Knitting of 3D Meshes

We present the first computational approach that can transform 3D meshes, created by traditional modeling programs, directly into instructions for a computer-controlled knitting machine.Knitting machines are able to robustly and repeatably form knitted 3D surfaces from yarn, but have many constraints on what they can fabricate. Given user-defined starting and ending points on an input mesh, our system incrementally builds a helix-free, quad-dominant mesh with uniform edge lengths, runs a tracing procedure over this mesh to generate a knitting path, and schedules the knitting instructions for this path in a way that is compatible with machine constraints. We demonstrate our approach on a wide range of 3D meshes.

Active Animations of Reduced Deformable Models with Environment Interactions

We present an efficient spacetime optimization method to automatically generate animations for a general volumetric, elastically deformable body. Our approach can model the interactions between the body and the environment and automatically generate active animations. We model the frictional contact forces using contact invariant optimization and the fluid drag forces using a simplified model. To handle complex objects, we use a reduced deformable model and present a novel hybrid optimizer to search for the local minima efficiently. This allows us to use long-horizon motion planning to automatically generate animations such as walking, jumping, swimming, and rolling. We evaluate the approach on different shapes and animations, including deformable body navigation and combining with an open-loop controller for realtime forward simulation.

Exposure: A White-Box Photo Post-Processing Framework

Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this paper a deep learning approach that is instead trained on unpaired data, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits, rather than just a "black box" result. Through quantitative comparisons and user studies, we show that our retouching results surpass those of strong baselines.

Real-time High-accuracy 3D Reconstruction with Consumer RGB-D Cameras

We present an integrated approach for reconstructing high-fidelity 3D models using consumer RGB-D cameras. RGB-D registration and reconstruction algorithms are prone to errors from scanning noise, making it hard to perform 3D reconstruction accurately. The key idea of our method is to assign a probabilistic uncertainty model to each depth measurement, which then guides the scan alignment and depth fusion. This allows us to effectively handle inherent noise and distortion in depth maps while keeping the overall scan registration procedure under the iterative closest point (ICP) frame-work for simplicity and efficiency. We further introduce a local-to-global, submap-based, and uncertainty-aware global pose optimization scheme to improve scalability and guarantee global model consistency. Finally, we have implemented the proposed algorithm on the GPU, achieving real-time 3D scanning frame rates and updating the reconstructed model on-the-fly. Experimental results on simulated and real-world data demonstrate that the proposed method outperforms state-of-the-art systems in terms of the accuracy of both recovered camera trajectories and reconstructed models.

Natural Boundary Conditions for Smoothing in Geometry Processing

In geometry processing, smoothness energies are commonly used to model scattered data interpolation, dense data denoising, and regularization during shape optimization. The squared Laplacian energy is a popular choice of energy and has a corresponding standard implementation: squaring the discrete Laplacian matrix. For compact domains, when values along the boundary are not known in advance, this construction bakes in low-order boundary conditions. This causes the geometric shape of the boundary to strongly bias the solution. For many applications, this is undesirable. Instead, we propose using the squared Frobenius norm of the Hessian as a smoothness energy. Unlike the squared Laplacian energy, this energy's natural boundary conditions (those that best minimize the energy) correspond to meaningful high-order boundary conditions. These boundary conditions model free boundaries where the shape of the boundary should not bias the solution locally. Our analysis begins in the smooth setting and concludes with discretizations using finite-differences on 2D grids or mixed finite elements for triangle meshes. We demonstrate the core behavior of the squared Hessian as a smoothness energy for various tasks.

Efficient and Accurate Collision Response for Elastically Deformable Models

Simulating (elastically) deformable models that can collide with each other and with the environment remains a challenging task. The resulting contact problems can be elegantly approached using Lagrange multipliers to represent the unknown magnitude of the response forces. Typical methods construct and solve a Linear Complementarity Problem (LCP) to obtain the response forces. This requires the inverse of the generalized mass matrix, which is in general hard to obtain for deformable-body problems. In this paper we tackle such contact problems by directly solving the Mixed Linear Complementarity Problem (MLCP) and omitting the construction of an LCP matrix. Since the MLCP is equivalent to a convex quadratic program subject to inequality constraints, we propose to use a Conjugate Residual (CR) solver as the backbone of our collision-response system. We also propose a simple yet efficient preconditioner that ensures faster convergence. Finally, our approach is faster than existing methods (at the same accuracy), and it allows accurate treatment of friction.

Sony Pictures Imageworks Arnold

Sony Imageworks implementation of the Arnold renderer is a fork of the commercial product of the same name, which has evolved independently since around 2009. This paper focuses on the design choices that are unique to this version and have tailored the renderer to the specific requirements of film rendering at our studio. We detail our approach to subdivision surface tessellation, hair rendering, sampling and variance reduction techniques, as well as a description of our open source texturing and shading language components. We also discuss some ideas we once implemented but have since discarded to highlight the evolution of the software over the years

The Design and Evolution of Disney's Hyperion Renderer

Walt Disney Animation Studios has transitioned to path-traced global illumination as part of a progression of brute-force physically based rendering in the name of artist efficiency. To achieve this without compromising our geometric or shading complexity, we built our Hyperion renderer based on a novel architecture that extracts traversal and shading coherence from large, sorted ray batches. In this paper we describe our architecture and discuss our design decisions. We also explain how we are able to provide artistic control in a physically based renderer, and we demonstrate through case studies how we have benefited from having a proprietary renderer that can evolve with production needs.

Second-Order Occlusion-Aware Volumetric Radiance Caching

Accurate simulation of light transport in participating media is expensive. However, given the band-limiting effect of scattering in media, the radiance signal is in general smooth. This makes this kind of light transport very suitable for adaptive sampling and reconstruction techniques. In this work we present a novel algorithm for volumetric light transport. We adaptively sample or interpolate radiance from sparse points in the medium using a second-order Hessian-based error metric to determine when interpolation is appropriate. We derive our metric from each point's incoming light field, computed by using a proxy triangulation-based representation of the radiance reflected by the surrounding medium and geometry. We use this representation to efficiently compute the first- and second-order derivatives of the radiance at the cache points while accounting for occlusion changes. To validate the quality of our approach, we propose a self-contained two-dimensional model for light transport in media: We demonstrate in this space with reduced dimensionality that our work significantly outperforms previous radiance caching algorithms in terms of both derivatives estimates and final radiance extrapolation. Then we show how our results generalize to practical three-dimensional scenarios, where we show much better results while reducing computation time up to 30%.

MonoPerfCap: Human Performance Capture from Monocular Video

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.

Iterative Depth Warping and Its Applications

This article presents an iterative backward-warping technique and its applications. It predictively synthesizes depth buffers for novel views. Our solution is based on the fixed-point iteration that converges quickly in practice. Unlike this previous technique, our solution is a purely backward warping without using bidirectional sources. To efficiently seed the iterative process, we also propose a tight bounding method for motion vectors. Non-convergent depth holes are inpainted via deep depth buffers. Our solution works well with arbitrarily distributed motion vectors under moderate motions. Many scenarios can benefit from our depth warping. As an application, we propose a highly scalable image-based occlusion-culling technique, achieving a significant speedup compared to the state of the art. We also demonstrate the benefit of our solution in multi-view soft-shadow generation.

Dataset and metrics for predicting visible differences

A large number of imaging and computer graphics applications require localized information on the visibility of image distortions. Existing image quality metrics are not suitable for this task as they provide a single quality value per image. Existing visibility metrics produce visual difference maps, and are specifically designed for detecting just noticeable distortions but their predictions are often inaccurate. In this work, we argue that the key reason for this problem is the lack of large image collections with a good coverage of possible distortions that occur in different applications. To address the problem, we collect an extensive dataset of reference and distorted image pairs together with user markings indicating whether distortions are visible or not. We propose a statistical model that is designed for the meaningful interpretation of such data, which is affected by visual search and imprecision of manual marking. We use our dataset for training existing metrics and we demonstrate that their performance significantly improves. We show that our dataset with the proposed statistical model can be used to train a new CNN-based metric, which outperforms the existing solutions. We demonstrate the utility of such a metric in visually lossless JPEG compression, super-resolution and watermarking.

Directing Cinematographic Drones

Quadrotor drones equipped with high quality cameras have rapidely raised as novel, cheap and stable devices for filmmakers. While professional drone pilots can create aesthetically pleasing videos in short time, the smooth  and cinematographic  control of a camera drone remains challenging for most users, despite recent tools that either automate part of the process or enable the manual design of waypoints to create drone trajectories. This paper proposes to move a step further towards more accessible cinematographic drones by designing techniques to automatically or interactively plan quadrotor drone motions in 3D dynamic environments that satisfy both cinematographic and physical quadrotor constraints. We first propose the design of a Drone Toric Space as a dedicated camera parameter space with embedded constraints and derive some intuitive on-screen viewpoint manipulators. Second, we propose a specific path planning technique which ensures both that cinematographic properties can be enforced along the path, and that the path is physically feasible by a quadrotor drone. At last, we build on the Drone Toric Space and the specific path planning technique to coordinate the motion of multiple drones around dynamic targets. A number of results then demonstrate the interactive and automated capacities of our approaches on a number of use-cases.

RenderMan: An Advanced Path Tracing Architecture for Movie Rendering

Pixar's RenderMan renderer is used to render all of Pixar's films, and by many film studios to render visual effects for live-action movies. RenderMan started as a scanline renderer based on the Reyes algorithm, and was extended over the years with ray tracing and several global illumination algorithms. This paper describes the modern version of RenderMan, a new architecture for an extensible and programmable path tracer with many features that are essential to handle the fiercely complex scenes in movie production. Users can write their own materials using a bxdf interface, and their own light transport algorithms using an integrator interface -- or they can use the materials and light transport algorithms provided with RenderMan. Complex geometry and textures are handled with efficient multi-resolution representations, with resolution chosen using path differentials. We trace rays and shade ray hit points in medium-sized groups, which provides the benefits of SIMD execution without excessive memory overhead or data streaming. The path-tracing architecture handles surface, subsurface, and volume scattering. We show examples of the use of path tracing, bidirectional path tracing, VCM, and UPBP light transport algorithms. We also describe our progressive rendering for interactive use and our adaptation of denoising techniques.

Automatically Distributing Eulerian and Hybrid Fluid Simulations in the Cloud

Distributing a simulation across many machines can drastically speed up computations and increase detail. The computing cloud provides tremendous computing resources, but weak service guarantees force programs to manage significant system complexity: nodes, networks, and storage occasionally perform poorly or fail. We describe Halo, a system that automatically distributes grid-based and hybrid simulations across cloud computing nodes. The main simulation loop is written as simple sequential code and launches distributed computations across many cores. The simulation on each core runs as if it is stand-alone: Halo automatically stitches these multiple simulations into a single, larger one. To do this efficiently, Halo introduces a four-layer data model that translates between the contiguous, geometric objects used by simulation libraries and the replicated, fine-grained objects managed by its underlying cloud computing runtime. Using PhysBAM particle level-set fluid simulations, we demonstrate that Halo can run higher detail simulations faster, distribute simulations on up to 512 cores and run enormous simulations ($1024^3$ cells). Halo automatically manages these distributed simulations, balancing load across nodes and recovering from failures. Implementations of PhysBAM water and smoke simulations as well as an open source heat-diffusion simulations show Halo is general and can support complex simulations.

Height-Field Block Decomposition

Subtractive manufacturing technologies, such as 3-axis milling, add a useful tool to the digital manufacturing arsenal. However, each milling pass using such machines can only carve a single height-field, defined with respect to the machine tray. We enable fabrication of general shapes using 3-axis milling, providing a robust algorithm to decompose any shape into a few height-field blocks. Such blocks can be manufactured with a single milling pass and then assembled to form the target geometry. Computing such decompositions requires solving a complex discrete optimization problem, variants of which are known to be NP-hard. We propose a two-step process, based on the observation that if the height-field directions are constrained to the major axes we can guarantee a valid decomposition starting from a suitable surface segmentation. Our method first produces a compact set of large, possibly overlapping, height-field blocks that jointly cover the model surface. We then compute an overlap-free decomposition via a combination of cycle elimination and topological sorting on a graph. The algorithm produces a compact set of height-field blocks that jointly describe the input model and satisfy all manufacturing constraints. We demonstrate our method on a range of inputs, and showcase several real life models manufactured using our technique.

Manuka: A batch-shading architecture for spectral path tracing in movie production

The Manuka rendering architecture has been designed to enable visually rich computer generated imagery for visual effects in movie production. This means supporting extremely complex geometry, texturing and shading. In the current generation of renderers, it is essential to support very accurate global illumination as a means to naturally tie together different assets in a picture. This is achieved with Monte Carlo path tracing, using a paradigm often called shade on hit, in which the renderer alternates tracing rays with running shaders on the various ray hits. The shaders take the role of generating the inputs of the local material structure which is then used by path sampling logic to evaluate contributions and to inform what further rays to cast through the scene. We propose a shade before hit paradigm instead and minimise I/O strain on the system, leveraging locality of reference by running pattern generation shaders before we execute light transport simulation by path sampling. We describe a full architecture built around this approach, featuring spectral light transport and a flexible implementation of multiple importance sampling, resulting in a system able to support a comparable amount of extensibility to what made the reyes rendering architecture successful over many decades.

Arnold: A Brute-force Production Path Tracer

Arnold is a physically-based renderer for feature-length animation and visual effects. Arnold has been created to take on the challenge of making the simple and elegant approach of brute-force Monte Carlo path tracing practical for production rendering. Achieving this requires building a robust piece of ray tracing software that can ingest large amounts of geometry with detailed shading and lighting and produce images with high fidelity, while scaling well with the available memory and processing power. Arnold's guiding principles are to expose as few controls as possible, provide rapid feedback to artists, and adapt to various production workflows. In this paper we describe its architecture with a focus on the design and implementation choices made during its evolutionary development to meet the aforementioned requirements and goals. Arnold's workhorse is a unidirectional path tracer that avoids the use of hard to manage and artifact-prone caching and sits on top of a ray tracing engine optimized to shoot and shade billions of spatially incoherent rays throughout a scene. A comprehensive API provides the means to configure and extend the system's functionality, to describe a scene, render it, and write the results to dis

Spoke-Darts for High-Dimensional Blue Noise Sampling

Blue noise sampling has been adopted for many graphics applications, but mostly in low dimensional spaces. Extensions to higher dimensional spaces remain under-explored. We present a blue noise sampling approach with good quality and performance across different dimensions. This is achieved by spoke-dart sampling, combining the locality of advancing front with the dimension-reduction of hyperplanes: samples are chosen from radial line segments through prior samples. New sampling rules over these segments create step blue noise, with a flat frequency spectrum, which avoids artifacts in high-frequency regions. Our algorithm creates blue noise directly and scales well to high dimensions. This enables several applications such as Delaunay graph construction, global optimization, rendering, and motion planning.

A Quantitative Perceptual Model for Tactile Roughness

Everyone uses the sense of touch to explore the world, and roughness is one of the most important qualities in tactile perception. Roughness is a major identifier for judgments of material composition, comfort and friction, and it is tied closely to manual dexterity. The advent of high-resolution 3D printing technology provides the ability to fabricate arbitrary 3D textures with surface geometry that confers haptic properties. In this work, we address the problem of mapping object geometry to tactile roughness. We fabricate a set of carefully designed stimuli and use them in experiments with human subjects to build a perceptual space for roughness. We then match this space to a quantitative model obtained from strain fields derived from elasticity simulations of the human skin contacting the texture geometry, drawing from past research in neuroscience and psychophysics. We demonstrate how this model can be applied to predict and alter surface roughness, and we show several applications in the context of fabrication.

Bibliometrics

Publication Years 1982-2018
Publication Count 3148
Citation Count 93763
Available for Download 3146
Downloads (6 weeks) 36916
Downloads (12 Months) 361486
Downloads (cumulative) 3668841
Average downloads per article 1166
Average citations per article 30
First Name Last Name Award
Aseem Agarwala ACM Doctoral Dissertation Award
Honorable Mention (2006) ACM Doctoral Dissertation Award
Honorable Mention (2006)
Narendra Ahuja ACM Fellows (1996)
Kurt B Akeley ACM Fellows (1996)
Ronald M Baecker ACM Fellows (2011)
Chandrajit L. Bajaj ACM Fellows (2009)
Alan H Barr ACM Fellows (1995)
Benjamin B Bederson ACM Distinguished Member (2011)
Kellogg S. Booth ACM Distinguished Member (2009)
ACM Senior Member (2009)
Alan H Borning ACM Fellows (2001)
Ronan Boulic ACM Senior Member (2006)
Achi Brandt SIAM/ACM Prize in Computational Science and Engineering (2005)
Frederick Brooks ACM-IEEE CS Eckert-Mauchly Award (2004)
ACM A. M. Turing Award (1999)
ACM AAAI Allen Newell Award (1994)
ACM Fellows (1994)
ACM Distinguished Service Award (1987)
William A.S. Buxton ACM Fellows (2008)
Stuart K. Card ACM Fellows (2000)
Bernard Chazelle ACM Fellows (1996)
Bing-Yu Chen ACM Senior Member (2011)
Michael F. Cohen ACM Fellows (2007)
Rob Cook ACM Fellows (1999)
Seth Cooper ACM Doctoral Dissertation Award (2011)
Thomas DeFanti ACM Fellows (1994)
Outstanding Contribution to ACM Award (1988)
James Demmel ACM Paris Kanellakis Theory and Practice Award (2014)
ACM Fellows (1999)
David P Dobkin ACM Fellows (1997)
Fredo Durand ACM Fellows (2016)
David S Ebert ACM Senior Member (2009)
Alexei A. Efros ACM Prize in Computing (2016)
David Eppstein ACM Fellows (2011)
Irfan A Essa ACM Senior Member (2008)
Adam Finkelstein ACM Fellows (2014)
James D Foley ACM Fellows (1999)
David Forsyth ACM Fellows (2013)
William Freeman ACM Fellows (2016)
Henry Fuchs ACM Fellows (1995)
Robert M Geist ACM Distinguished Member (2009)
Michael L Gleicher ACM Distinguished Member (2011)
Mashhuda Glencross ACM Senior Member (2017)
Shyamnath Gollakota ACM Doctoral Dissertation Award (2012)
Donald Greenberg ACM Fellows (1995)
Markus Gross ACM Fellows (2012)
Leonidas J Guibas ACM AAAI Allen Newell Award (2007)
ACM Fellows (1999)
Baining Guo ACM Fellows (2011)
John Guttag ACM Fellows (2006)
Pat Hanrahan ACM Fellows (2008)
David Harel ACM Software System Award (2007)
ACM Fellows (1994)
ACM Karl V. Karlstrom Outstanding Educator Award (1992)
Haitham Hassanieh ACM Doctoral Dissertation Award (2016)
Aaron Hertzmann ACM Distinguished Member (2015)
Derek Hoiem ACM Doctoral Dissertation Award
Honorable Mention (2008) ACM Doctoral Dissertation Award
Honorable Mention (2008)
Hugues Hoppe ACM Fellows (2011)
Mark A Horowitz ACM Fellows (2003)
Hui Huang ACM Senior Member (2016)
Warren Alva Hunt ACM Distinguished Member (2015)
ACM Senior Member (2013)
Victoria L Interrante ACM Senior Member (2007)
Robert J.K. Jacob ACM Fellows (2016)
Odest Chadwicke Jenkins ACM Senior Member (2017)
Takeo Kanade ACM AAAI Allen Newell Award (2010)
ACM Fellows (1999)
Michael Kass ACM Fellows (2017)
Dina Katabi ACM Prize in Computing (2017)
ACM Fellows (2013)
ACM Grace Murray Hopper Award (2012)
ACM Doctoral Dissertation Award
Honorable Mention (2003)
Donald E Knuth ACM Fellows (1994)
ACM Software System Award (1986)
ACM A. M. Turing Award (1974)
ACM Grace Murray Hopper Award (1971)
Daphne Koller ACM Prize in Computing (2007)
Marc Levoy ACM Fellows (2007)
Henry M Levy ACM Fellows (1996)
Ming C Lin ACM Fellows (2011)
Jock D Mackinlay ACM Distinguished Member (2013)
Dinesh Manocha ACM Fellows (2009)
Michael Mitzenmacher ACM Fellows (2014)
J Nievergelt ACM Fellows (1995)
James F. O'Brien ACM Distinguished Member (2009)
Dan R Olsen ACM Fellows (2006)
Ming Ouhyoung ACM Senior Member (2007)
John Douglas Owens ACM Distinguished Member (2017)
Renato Pajarola ACM Senior Member (2016)
Franco P Preparata ACM Fellows (1995)
Abtin Rahimian ACM Gordon Bell Prize
Performance (2010) ACM Gordon Bell Prize
Performance (2010)
Ravi Ramamoorthi ACM Fellows (2017)
ACM Distinguished Member (2015)
Sandy Ressler ACM Senior Member (2009)
Jarek Rossignac ACM Senior Member (2013)
Holly E Rushmeier ACM Fellows (2016)
ACM Distinguished Member (2006)
David H Salesin ACM Fellows (2002)
Hanan Samet ACM Paris Kanellakis Theory and Practice Award (2011)
ACM Fellows (1996)
Peter Schroeder ACM Fellows (2015)
Micha Sharir ACM Fellows (1997)
Stuart Shieber ACM Fellows (2014)
Ben Shneiderman ACM Fellows (1997)
Jeremy Sugerman ACM Software System Award (2009)
Subhash Suri ACM Fellows (2010)
ACM Distinguished Member (2007)
Ivan Sutherland ACM Fellows (1994)
ACM Software System Award (1993)
ACM A. M. Turing Award (1988)
Richard Szeliski ACM Fellows (2008)
Shang-Hua Teng ACM Fellows (2009)
Demetri Terzopoulos ACM Fellows (2007)
Matthew A Turk ACM Senior Member (2007)
Baba C Vemuri ACM Fellows (2009)
Jeffrey S Vetter ACM Distinguished Member (2012)
ACM Gordon Bell Prize
Performance (2010)
J Turner Whitted ACM Fellows (1995)
Kai Xu ACM Senior Member (2017)
KangKang Yin ACM Senior Member (2016)
Eugene Zhang ACM Senior Member (2010)
Lixia Zhang ACM Fellows (2006)
Yuanyuan Zhou ACM Fellows (2013)
ACM Distinguished Member (2011)
Denis Zorin ACM Gordon Bell Prize
Performance (2010) ACM Gordon Bell Prize
Performance (2010)
Michael J Zyda ACM Senior Member (2009)
Andries van Dam ACM Fellows (1994)
ACM Karl V. Karlstrom Outstanding Educator Award (1993)
Andries van Dam ACM Fellows (1994)
ACM Karl V. Karlstrom Outstanding Educator Award (1993)

First Name Last Name Paper Counts
Daniel Cohen-Or 88
Frédo Durand 77
Wojciech Matusik 72
Hans Seidel 65
Baining Guo 64
Markus Gross 62
Kun Zhou 57
Ravi Ramamoorthi 53
Niloy Mitra 50
Heungyeung Shum 43
Jessica Hodgins 37
Leonidas Guibas 35
Ramesh Raskar 34
Eitan Grinspun 34
Szymon Rusinkiewicz 34
Alla Sheffer 33
Steve Marschner 33
Doug James 33
Maneesh Agrawala 33
Hugues Hoppe 32
Peter Wonka 32
Kavita Bala 32
Mathieu Desbrun 32
Fabio Pellacini 32
Pat Hanrahan 31
Mark Pauly 30
Henrik Jensen 29
James O'Brien 29
Yaron Lipman 28
Jovan Popović 28
Olga Sorkine-Hornung 27
Bernd Bickel 27
Hujun Bao 27
Takeo Igarashi 26
Zoran Popović 26
Wojciech Jarosz 26
Dani Lischinski 25
Daniele Panozzo 24
Shimin Hu 24
Derek Nowrouzezahrai 24
Richard Szeliski 24
Yizhou Yu 23
Hao Zhang 23
Oliver Deussen 23
Wolfgang Heidrich 23
Denis Zorin 23
Hanspeter Pfister 23
Shree Nayar 22
Michiel Van De Panne 22
Michael Cohen 22
Johannes Kopf 22
Adam Finkelstein 22
Tientsin Wong 22
Hui Huang 22
Jan Kautz 22
Greg Turk 21
Diego Gutierrez 21
Helmut Pottmann 20
Jaakko Lehtinen 20
Ronald Fedkiw 20
George Drettakis 20
Peter Schröder 20
Eli Shechtman 20
Vladlen Koltun 20
Wilmot Li 20
Liyi Wei 20
Thomas Funkhouser 19
Stelian Coros 19
Pieter Peers 19
Sylvain Lefebvre 19
Robert Bridson 19
Karol Myszkowski 19
Jason Lawrence 19
David Salesin 19
Bernhard Thomaszewski 19
Christian Theobalt 19
Dinesh Pai 18
Raanan Fattal 18
Aseem Agarwala 18
Dinesh Manocha 18
Matthias Zwicker 18
Chris Wojtan 18
Matthias Nießner 18
Marc Alexa 18
Ariel Shamir 18
Bedřich Beneš 17
William Freeman 17
Ladislav Kavan 17
Paul Debevec 17
Gordon Wetzstein 16
Julie Dorsey 16
C Liu 16
Jinxiang Chai 16
Leif Kobbelt 16
Olga Sorkine 16
Changxi Zheng 16
Sylvain Paris 16
Piotr Didyk 16
Tobias Ritschel 16
Tao Ju 15
Kai Xu 15
Ilya Baran 15
Jernej Barbič 15
Marie Cani 15
David Levin 15
Marco Tarini 15
John Snyder 15
Brian Curless 15
Andrei Sharf 15
Miguel Otaduy 15
Yue Dong 15
Tomas Akenine-Möller 15
Vladimir Kim 15
Bruno Lévy 15
Adrien Bousseau 15
Weiwei Xu 14
Diego Nehab 14
Toshiya Hachisuka 14
Tony DeRose 14
Xin Tong 14
Marc Levoy 14
Ming Lin 14
Alec Jacobson 14
Wenzel Jakob 14
Jaroslav Křivánek 14
Thabo Beeler 14
Stephen Lin 14
Bruce Walter 14
Danny Kaufman 14
Nils Thuerey 14
Yang Liu 14
Nathan Carr 14
Baoquan Chen 14
Shuang Zhao 14
Ofir Weber 13
Michael Zollhöfer 13
Shahram Izadi 13
Eftychios Sifakis 13
Huamin Wang 13
Andrew Selle 13
Wenping Wang 13
Chikeung Tang 13
Matthias Hullin 13
Connelly Barnes 13
Carol O'Sullivan 13
Kalyan Sunkavalli 13
Douglas Lanman 13
Jian Sun 13
Tim Weyrich 13
Matthew Fisher 13
Donald Greenberg 13
Steven Seitz 12
Aaron Hertzmann 12
Ulrich Pinkall 12
Michael Goesele 12
Singbing Kang 12
Michael Kazhdan 12
Hao Zhang 12
Jin Huang 12
Fernando De Goes 12
Marc Stamminger 12
Daniel Aliaga 12
Christopher Batty 12
Etienne Vouga 12
Johannes Wallner 12
Holly Rushmeier 12
Alexei Efros 12
Timo Aila 12
Yotam Gingold 12
Karan Singh 12
Craig Gotsman 12
Alexander Hornung 12
Pierre Alliez 12
Xin Tong 12
JeHee Lee 12
Oliver Van Kaick 12
Kayvon Fatahalian 12
Jonathan Ragan-Kelley 12
Elmar Eisemann 12
Dan Goldman 12
Rasmus Tamstorf 11
Jiaya Jia 11
Felix Heide 11
Derek Bradley 11
Shinjiro Sueda 11
Keenan Crane 11
Maks Ovsjanikov 11
Scott Schaefer 11
KangKang Yin 11
David Luebke 11
Adrien Treuille 11
Rahul Narain 10
David Bommes 10
Adam Bargteil 10
Rafał Mantiuk 10
Minglun Gong 10
Baoquan Chen 10
Tongyee Lee 10
Yingqing Xu 10
John Hart 10
Pitchaya Sitthi-amorn 10
Justin Solomon 10
Andrew Adams 10
Jiaping Wang 10
Markus Gross 10
Robert Sumner 10
Philip Dutré 10
Joseph Teran 10
Carsten Dachsbacher 10
Abhijeet Ghosh 10
François Sillion 10
Yiying Tong 10
Sylvain Paris 10
Ruizhen Hu 9
Noam Aigerman 9
Christian Theobalt 9
Nicolas Ray 9
Pradeep Sen 9
Michael Gleicher 9
Michael Black 9
Evangelos Kalogerakis 9
Jue Wang 9
Paolo Cignoni 9
Phengann Heng 9
Joe Warren 9
Mirela Ben-Chen 9
Nico Pietroni 9
Lingqi Yan 9
Qiming Hou 9
Min Kim 9
Rui Wang 9
Jarek Rossignac 9
Marcel Campen 9
Nancy Pollard 9
Amir Vaxman 9
Hao Li 9
Pascal Barla 9
Jan Novák 9
Hendrik Lensch 9
Todd Zickler 8
Roi Poranne 8
Chiwing Fu 8
Charles Loop 8
Marcus Magnor 8
Martin Kilian 8
Eugene Zhang 8
Edward Adelson 8
Victor Ostromoukhov 8
François Faure 8
Long Quan 8
Tomoyuki Nishita 8
Peter Belhumeur 8
Mark Horowitz 8
Nuttapong Chentanez 8
Sofien Bouaziz 8
Matt Uyttendaele 8
Michael Wimmer 8
Michael Garland 8
James Tompkin 8
Aljoscha Smolić 8
Cyril Soler 8
Craig Donner 8
Miloš Hašan 8
Saikit Yeung 8
Chongyang Ma 8
Tunç Aydın 8
Daniel Sýkora 8
Demetri Terzopoulos 8
Hongbo Fu 8
Eugene Fiume 8
Peter Sloan 8
Manuel Oliveira 8
Robert Sumner 8
Tamy Boubekeur 8
Ping Tan 8
Qixing Huang 8
Jörg Peters 8
Michael Rubinstein 7
Gilbert Bernstein 7
Linjie Luo 7
Emily Whiting 7
Ravi Ramamoorthi 7
Dongming Yan 7
Xin Tong 7
Oliver Wang 7
Theodore Kim 7
Stephen DiVerdi 7
Duygu Ceylan 7
Ayellet Tal 7
Belen Masia 7
Laurent Belcour 7
Tiantian Liu 7
Matthias Müller 7
Dinesh Manocha 7
Doug Decarlo 7
Anton Kaplanyan 7
Ron Goldman 7
Peter Shirley 7
Nima Kalantari 7
Nicholas Vining 7
Moritz Bächer 7
Sunghee Lee 7
Tianjia Shao 7
John Lewis 7
John Hughes 7
John Owens 7
Floraine Berthouzoz 7
Noah Snavely 7
Ligang Liu 7
Junyong Noh 7
Yoshinori Dobashi 7
Mario Botsch 7
Marc Pollefeys 7
Shiqing Xin 7
Sören Pirk 7
Brian Barsky 7
Petrik Clarberg 7
Taku Komura 7
Ariel Shamir 7
David Forsyth 7
Paul Beardsley 7
Daniel Vlasic 7
William Mark 7
Brian Wyvill 7
Yaser Sheikh 6
Yongjoon Lee 6
Tim Hawkins 6
Ligang Liu 6
Kurt Akeley 6
David Cohen-Steiner 6
Honghua Li 6
Zhong Ren 6
Anat Levin 6
John Keyser 6
Frank Losasso 6
Pedro Sander 6
Andrea Tagliasacchi 6
Ken Museth 6
Gabriel Brostow 6
Boris Neubert 6
Hao Su 6
Jacob Munkberg 6
Hyeongseok Ko 6
Xinguo Liu 6
Srinivasa Narasimhan 6
Martin Banks 6
Ondřej Jamriška 6
Zhigang Deng 6
Menglei Chai 6
Mark Meyer 6
Erik Reinhard 6
Okan Arikan 6
Samuel Hasinoff 6
Sungeui Yoon 6
Rachel McDonnell 6
Wenping Wang 6
Matthew Hirsch 6
Valerio Pascucci 6
Max Wardetzky 6
Yongliang Yang 6
Leonard McMillan 6
Aaron Hertzmann 6
Roger Hersch 6
Lapfai Yu 6
Yuting Ye 6
Cem Yuksel 6
Derek Bradley 6
Hao Li 6
Samuli Laine 6
James Ferwerda 6
Ivo Ihrke 6
Chiewlan Tai 6
Kari Pulli 6
Qionghai Dai 6
Thomas Funkhouser 6
Chenfanfu Jiang 6
Nobuyuki Umetani 6
Yonghao Yue 6
Klaus Hildebrandt 6
Shimin Hu 6
Florence Bertails-Descoubes 6
Elaine Cohen 6
Chandrajit Bajaj 6
Jon Hasselgren, 6
Shahar Kovalsky 6
Bart Adams 6
David Dobkin 6
Solomon Boulos 6
Ersin Yumer 6
Enrico Puppo 6
Eric Heitz 6
Peng Song 6
Ibraheem Alhashim 6
Stefan Jeschke 6
Tim Foley 6
Andrew Nealen 6
Mélina Skouras 6
Zeev Farbman 6
Xiaohan Shi 5
Arno Zinke 5
Fabrice Rousselle 5
Adam Arbree 5
Julien Pettré 5
Youngjun Kim 5
Charles Zitnick 5
Xin Tong 5
Iman Sadeghi 5
Loïc Barthe 5
Kevin Wampler 5
Carsten Dachsbacher 5
Aaron Lefohn 5
Chen Greif 5
Miika Aittala 5
Daniel Cohen-Or 5
Amit Bermano 5
Nicolas Bonneel 5
Rui Wang 5
Yanlin Weng 5
Xin Sun 5
Michael Wand 5
Nils Thürey 5
Thorsten Thormählen 5
Gaël Guennebaud 5
Yungyu Chuang 5
Ignacio Garcia-Dorado 5
Philipp Slusallek 5
Paul Kry 5
Feng Xu 5
Siddhartha Chaudhuri 5
Hao Zhang 5
Chenglei Wu 5
Paul Asente 5
Paul Guerrero 5
Anjul Patney 5
Sebastian Martin 5
James Hays 5
Christoph Von Tycowicz 5
Igor Mordatch 5
Carsten Rother 5
James McCann 5
Jonathan Shewchuk 5
Yushuen Wang 5
Johannes Schmid 5
Jay Busch 5
Kiriakos Kutulakos 5
Jérémie Dumas 5
Greg Ward 5
Xiaoming Fu 5
Makoto Okabe 5
Manolis Savva 5
Adrian Jarabo 5
John Anderson 5
Alec Rivers 5
David Harmon 5
Ren Ng 5
Ravish Mehra 5
Johannes Hanika 5
Jack Tumblin 5
Martin Šik 5
Craig Schroeder 5
Changxi Zheng 5
Tobias Pfaff 5
Joëlle Thollot 5
Andreas Weber 5
Jonathantyler Moon 5
Simon Fuhrmann 5
Naga Govindaraju 5
Graham Fyffe 5
Reinhard Klein 5
Romain Vergne 5
Breannan Smith 5
Jun Mitani 5
Martin Bokeloh 5
Nicolas Holzschuch 5
Yingge Qu 5
Shai Avidan 5
David Levin 5
Thomas Sederberg 5
Olga Sorkine 5
Patrick Mullen 5
Michael Nielsen 5
Adriana Schulz 5
Oliver Bimber 5
Aron Monszpart 5
Siddhartha Chaudhuri 5
Sunil Hadap 5
Gaurav Chaurasia 5
Ying He 5
Bingyu Chen 5
Seungyong Lee 5
Christian Schulz 5
Roland Fleming 5
Mingming Cheng 5
Amit Agrawal 5
Martin Fuchs 5
Aleksey Golovinskiy 5
Norman Badler 5
Jianyuan Min 5
Brian Guenter 5
Miloš Hašan 5
Libin Liu 5
Christoph Bregler 5
Gabriel Taubin 5
James Gregson 5
Niloy Mitra 5
Min Tang 5
Ashish Myles 5
Sebastian Thrun 5
Ralph Martin 5
Jianmin Zheng 5
Ulf Assarsson 5
Andrew Jones 5
Yiying Tong 5
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Desai Chen 5
Abe Davis 5
Ramesh Raskar 5
Ares Lagae 5
Lvdi Wang 5
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Yanir Kleiman 4
Alexey Stomakhin 4
Gerard Pons-Moll 4
Shihong Xia 4
Benjamin Gilles 4
Kun Xu 4
Jonàs Martínez 4
Otmar Hilliges 4
Vitor Pamplona 4
Peter Kaufmann 4
Soheil Darabi 4
Steffen Weißmann 4
Jeongmo Hong 4
David Jacobs 4
Emily Cooper 4
Yanyun Chen 4
Jos Stam 4
Billy Chen 4
Ken Perlin 4
Art Tevs 4
Daniel Cohen-Or 4
Satoshi Iizuka 4
Yukun Lai 4
Simon Heinzle 4
Mathieu Desbrun 4
Miles Macklin 4
Fuchung Huang 4
Angela Dai 4
Kenny Mitchell 4
Jonathan Dupuy 4
Jie Tan 4
Mehmet Yümer 4
Peiran Ren 4
Hao Pan 4
Soham Mehta 4
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Tamar Shinar 4
John Snyder 4
Steven Drucker 4
Mikhail Bessmeltsev 4
Kai Hormann 4
Yin Li 4
Scott Hudson 4
Jiawen Chen 4
Yueshan Xiong 4
Peer Bremer 4
Mark Meyer 4
Robert Klassen 4
Jiří Vorba 4
Jiaxian Yao 4
Ralph Martin 4
William Cowan 4
Adrian Butscher 4
Bin Wang 4
Chen Cao 4
Nikunj Raghuvanshi 4
Dingzeyu Li 4
Beibei Liu 4
Leif Kobbelt 4
Wolfgang Heidrich 4
Guofu Xie 4
Kartic Subr 4
Greg Humphreys 4
Lakulish Antani 4
Anselm Grundhöfer 4
Xi Wang 4
Carl Schissler 4
Xueming Yu 4
Eugene D'Eon 4
Victor Zordan 4
Jungdam Won 4
Theodore Gast 4
Felix Knöppel 4
Gabriel Peyré 4
Kenshi Takayama 4
Bochang Moon 4
Marios Papas 4
Michael Schwarz 4
Moshe Mahler 4
Naureen Mahmood 4
Liwen Hu 4
Xueting Liu 4
Ronen Basri 4
Li Xu 4
Noa Fish 4
Levi Valgaerts 4
Ashok Veeraraghavan 4
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Weiwen Feng 4
Binhhuy Le 4
Sungyong Shin 4
Rodolfo Lima 4
Dmitry Sokolov 4
Holger Theisel 4
Antonio Criminisi 4
Dan Olsen 4
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Yeongho Seol 4
Neil Dodgson 4
Gilles Daviet 4
Uri Ascher 4
Rémi Ronfard 4
Ian McDowall 4
Shihao Wu 4
Neal Wadhwa 4
Zhili Chen 4
Edilson De Aguiar 4
Pramook Khungurn 4
Alexander Schiftner 4
Christopher Twigg 4
Jason Sewall 4
Wenping Wang 4
Liangliang Nan 4
Qixing Huang 4
Pierre Laffont 4
Claude Knaus 4
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Narendra Ahuja 4
Lei Yang 4
Ligang Liu 4
Michal Lukáč 4
Hans Ebke 4
Kevin Egan 4
Taipang Wu 4
Nathan Mitchell 4
Jonathan Denning 4
Leventburak Kara 4
Jonas Unger 4
Jonathan Barron 4
Chengcheng Tang 4
Ming Lin 4
Barbara Solenthaler 4
Basile Audoly 4
Radomír Měch 4
Bo Sun 4
Andrew Fitzgibbon 4
Charles Han 4
Forrester Cole 4
Falai Chen 4
Zachary DeVito 4
Katherine Breeden 4
Chaohui Shen 4
Pedro Sander 4
Markus Steinberger 4
Dieter Schmalstieg 4
Martin Wicke 4
James Hegarty 4
Ran Gal 4
Robert Cook 4
Michael Neff 4
Akash Garg 4
Hongzhi Wu 4
Matthew O'Toole 4
Ming Chuang 4
A Öztireli 4
Pascal Müller 4
Javier Romero 4
Yong He 4
Cláudio Silva 4
Pablo Garrido 4
Damien Rohmer 4
Stéfanie Hahmann 4
Jack Wang 4
Michael Burns 4
Philippe Beaudoin 4
Carsten Stoll 4
Geoffrey Irving 4
Yebin Liu 4
Edgar Simo-Serra 4
Ari Rappoport 4
John Beatty 4
Michael Brown 4
Dhruv Mahajan 4
Timothy Langlois 4
Jue Wang 4
Fanglue Zhang 4
Jakub Fišer 4
Leonid Sigal 4
Mridul Aanjaneya 4
Lin Lu 4
Aditi Majumder 4
Tamal Dey 4
Frédo Durand 4
Tiberiu Popa 4
C Liu 4
Rasmus Barringer 4
Mira Dontcheva 4
Neel Joshi 4
Lee Markosian 4
Juyong Zhang 4
Yağiz Aksoy 4
Hiroshi Ishikawa 4
Liqian Ma 3
Philip Shilane 3
Helge Seetzen 3
Chenyang Zhu 3
Julian Panetta 3
Albert Chern 3
Ioannis Gkioulekas 3
Yufeng Zhu 3
Ryoichi Ando 3
Marco Livesu 3
Jianchao Tan 3
Ming Zou 3
Amitabh Varshney 3
Yangyan Li 3
Yun Fei 3
Dan Casas 3
Ludovic Hoyet 3
Heinrich Bülthoff 3
Haibin Huang 3
Steven Gortler 3
Peter Sand 3
Takashi Ijiri 3
Tsuyoshi Yamamoto 3
Byunguck Kim 3
Hungkuo Chu 3
Abhinav Golas 3
Qunsheng Peng 3
Rynson Lau 3
Tomáš Davidovič 3
Sumit Jain 3
Florent Lafarge 3
Jongmin Baek 3
Bailin Deng 3
Paul Reitsma 3
Yuting Tsai 3
Aner Ben-Artzi 3
Bruce Gooch 3
Fuhua Cheng 3
Bianca Falcidieno 3
David Cline 3
Hengchin Yeh 3
Feng Sun 3
Dilip Krishnan 3
Jinxiang Chai 3
Michael Ashikhmin 3
Joshua Litven 3
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Hongzhi Wu 3
Sean Bell 3
Xuebin Peng 3
Gurprit Singh 3
Pierre Poulin 3
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Peter Vangorp 3
Junjie Cao 3
Kangxue Yin 3
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Yuchi Huo 3
Xiaobai Chen 3
Fabian Hahn 3
Jonathan Kaldor 3
Kristian Hildebrand 3
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Michael Kass 3
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Till Niese 3
Ying Cao 3
Jean Bazin 3
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Zhaoliang Lun 3
Jeroen Van Baar 3
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William Kerr 3
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Lifeng Wang 3
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Barry Joe 3
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Yili Zhao 3
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Yichang Shih 3
Bo Ren 3
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Affiliation Paper Counts
Naval Postgraduate School 1
Lahore University of Management Sciences 1
CPE Lyon 1
Vilnius University 1
American University of Beirut 1
Government College of Engineering Pune 1
CRAN Centre de Recherche en Automatique de Nancy 1
Clariant AG 1
Interval Research Corporation 1
New York Institute of Technology 1
Computer Sciences Corp. 1
LG Electronics, Korea 1
Alexandra Instituttet AS 1
Italian Institute of Technology 1
Intuitive Surgical 1
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Dolby Laboratories 2
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Ecole Normale Superieure 3
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Control Data Corporation 3
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Sharp Laboratories of America 3
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Eastman Kodak Company 3
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Xerox Corporation 3
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Rutherford Appleton Laboratory 3
Nokia USA 3
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Nippon Telegraph and Telephone Corporation 3
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Commonwealth Scientific and Industrial Research Organization 3
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University of Hull 3
Boeing Corporation 3
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Communaute d'Universites et d'Etablissements Lille Nord de France 3
Qatar Computing Research institute 3
Tokyo Institute of Technology 4
University of Adelaide 4
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Academic College of Tel-Aviv - Yaffo 4
Sun Yat-Sen University 4
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Portland State University 4
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Nokia Corporation 4
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Riken 4
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University of Glasgow 4
Chiba University 4
Samsung Electronics Co. Ltd. 4
University of Geneva 4
Laboratoire d'Analyse et d'Architecture des Systemes 4
VRVis Research Center 4
University of Electro-Communications 4
Ningbo University 5
Tampere University of Technology 5
Autodesk Inc. 5
Harvard School of Engineering and Applied Sciences 5
The Chinese University of Hong Kong, Shenzhen 5
University of Surrey 5
Universite Claude Bernard Lyon 1 5
Instituto Nacional de Matematica Pura E Aplicada, Rio de Janeiro 5
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HP Labs 5
IBM Research 5
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Kao Corporation 5
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Xi'an Jiaotong University 5
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Duke University 5
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Singapore University of Technology and Design 6
Laboratoire Jean Kuntzmann (LJK) 6
Tianjin University 6
Sony Corporation 6
University of Siegen 6
Osaka University 6
LORIA Laboratoire Lorrain de Recherche en Informatique et ses Applications 6
TELECOM ParisTech 6
University of Ulm 6
The Interdisciplinary Center Herzliya 6
University of Bielefeld 6
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Electronics Telecommunication Research Institute 6
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Facebook, Inc. 7
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Italian National Research Council 15
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Linkoping University 15
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Google Inc. 17
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Delft University of Technology 18
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Czech Technical University in Prague 18
Chinese Academy of Sciences 19
University of Illinois 19
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University of California, Santa Barbara 19
Beihang University 20
Universite de Lyon 21
IMAG 21
Technical University of Munich 21
University of Bern 22
Microsoft Research Cambridge 23
Rice University 23
University of Calgary 23
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National Cheng Kung University 24
University of Massachusetts Amherst 24
Brigham Young University 25
City University of Hong Kong 26
Max Planck Institute for Intelligent Systems 27
National Taiwan University 27
Lund University 27
National University of Singapore 28
Karlsruhe Institute of Technology 28
Charles University 29
University of Roma La Sapienza 29
University of Pennsylvania 30
Arizona State University 30
Graz University of Technology 31
King Abdullah University of Science and Technology 31
Ohio State University 32
Rutgers, The State University of New Jersey 32
INRIA Lorraine 33
National University of Defense Technology China 35
Yale University 36
Catholic University of Leuven, Leuven 36
Dartmouth College 38
Aalto University 38
Nanyang Technological University 39
Technical University of Darmstadt 39
Microsoft Corporation 40
University of Bonn 42
University of Montreal 43
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Brown University 44
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Technical University of Berlin 45
Saarland University 46
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Mitsubishi Electric Research Laboratories 61
Korea Advanced Institute of Science & Technology 61
INRIA Sophia Antipolis 62
Weizmann Institute of Science Israel 64
Vienna University of Technology 65
INRIA Institut National de Rechereche en Informatique et en Automatique 66
Intel Corporation 67
University of Utah 68
Seoul National University 68
Swiss Federal Institute of Technology, Lausanne 68
Texas A and M University 69
University of Science and Technology of China 70
Universite Grenoble Alpes 73
Hong Kong University of Science and Technology 73
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MIT Media Laboratory 77
University of California, San Diego 77
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The University of Hong Kong 86
University of Tokyo 96
Chinese University of Hong Kong 97
New York University 100
University of Toronto 100
Purdue University 100
University College London 110
California Institute of Technology 112
Georgia Institute of Technology 128
The University of North Carolina at Chapel Hill 136
Max Planck Institute for Informatics 140
Massachusetts Institute of Technology 158
University of Southern California 158
Tel Aviv University 163
Tsinghua University 188
University of Washington, Seattle 189
Princeton University 189
Carnegie Mellon University 190
MIT Computer Science and Artificial Intelligence Laboratory 191
University of California, Berkeley 208
Columbia University 208
Zhejiang University 228
Cornell University 237
The University of British Columbia 254
Microsoft Research 286
Microsoft Research Asia 287
Swiss Federal Institute of Technology, Zurich 334
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ACM Transactions on Graphics (TOG)
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2018
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2017
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2010
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2009
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2008
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2007
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Volume 13 Issue 4, Oct. 1994
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Volume 12 Issue 4, Oct. 1993
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Volume 11 Issue 4, Oct. 1992
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Volume 10 Issue 4, Oct. 1991
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Volume 10 Issue 1, Jan. 1991
Volume 9 Issue 4, Oct. 1990
Volume 9 Issue 3, July 1990
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Volume 9 Issue 1, Jan. 1990
Volume 8 Issue 4, Oct. 1989 Special issue on computer-aided design
Volume 8 Issue 3, July 1989
Volume 8 Issue 2, April 1989
Volume 8 Issue 1, Jan. 1989
Volume 7 Issue 4, Oct. 1988
Volume 7 Issue 3, July 1988
Volume 7 Issue 2, April 1988
Volume 7 Issue 1, Jan. 1988
Volume 6 Issue 4, Oct. 1987
Volume 6 Issue 3, July 1987
Volume 6 Issue 2, April 1987
Volume 6 Issue 1, Jan. 1987
Volume 5 Issue 4, Oct. 1986 Special issue on user interface software
Volume 5 Issue 3, July 1986
Volume 5 Issue 2, April 1986
Volume 5 Issue 1, Jan. 1986
Volume 4 Issue 4, Oct. 1985
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Volume 3 Issue 4, Oct. 1984
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