bluuebunny/crossref_metadata_2025 huggingface dataset:
Geological origins of seismic bright spot reflections in the Ordovician strata in the Halahatang area of the Tarim Basin, western China
Authors: Yang Wei , Gong Xiaoxing , Li Wenjie
Published: July 2018

Anomalously high-amplitude seismic reflections are commonly observed in deeply buried Ordovician carbonate strata in the Halahatang area of the northern Tarim Basin. These bright spots have been demonstrated to be generally related to effective oil and gas reservoirs. These bright spot reflections have complex geological origins, because they are deeply buried and have been altered by multi-phase tectonic movement and karstification. Currently, there is no effective geological model for these bright spots to guide hydrocarbon exploration and development. Using core, well logs, and seismic data, the geological origins of bright spot are classified into three types, controlled by karstification, faulting, and volcanic hydrothermal activity. Bright spots differing by geological origin exhibit large differences in seismic reflection character, such as reflection amplitude, curvature, degree of distortion, and the number of vertically stacked bright spots in the seismic section. By categorizing the bright spots and the seismic character of the surrounding strata, their geological origins can after be inferred. Reservoirs formed by early karstification were later altered by epigenetic karstification. Two periods of paleodrainage further altered the early dissolution pores. In addition, faults formed by tectonic uplift also enhanced the dissolution of the flowing karst waters. Some reservoirs were subsequently altered by Permian volcanic hydrothermal fluids.
Farmland abandonment in Europe: an overview of drivers, consequences, and assessment of the sustainability implications
Authors: Ustaoglu Eda , Collier Marcus J.
Published: July 2018

In the last decades, there have been large areas of agricultural land that were abandoned in Europe, producing significant social and environmental impacts. Land abandonment is a dynamic process, which is influenced by a complex range of drivers that vary over time and space. This process is driven by a combination of socio-economic, political, and environmental factors by which formerly cultivated fields are no longer economically viable under existing land-use and socio-economic conditions. The implications of land abandonment on biodiversity and other ecosystem services can be positive or negative depending on the conservation status of the area, agro-climatic conditions, and local factors. Therefore, the scope and extent of environmental impacts vary over time and location. Considering that land abandonment is a contentious issue in Europe, there is still growing need for research on this topic. This paper reviews (i) drivers and consequences of farmland abandonment in Europe, (ii) policy measures and tools developed by the European Union in relation to land abandonment process, (iii) the impacts and indicators that are used to assess ecosystem services that are related to land abandonment, and (iv) the methods by which socio-economic, environmental, and cultural values can be assessed. An overview of key impacts and indicators and the impact assessment methodologies will guide policy-making and planning processes that focus on sustainability impact assessment of land abandonment related to ecosystem services in Europe.
Heavy quark production inpAcollisions: The double parton scattering contribution
Authors: Cazaroto E. R. , Gonçalves V. P. , Navarra F. S.
Published: July 2018

In this paper, we estimate the double parton scattering (DPS) contribution for the heavy quark production in pA collisions at the LHC. The cross-sections for the charm and bottom production are estimated using the dipole approach, taking into account the saturation effects, which are important for high energies and for the scattering with a large nucleus. We compare the DPS contribution with the single parton scattering one and demonstrate that in the case of charm production, both are similar in the kinematical range probed by the LHC. Predictions for the rapidity range analyzed by the LHCb Collaboration are also presented. Our results indicate that the study of the DPS contribution for the heavy quark production in pPb collisions at the LHC is feasible and can be useful to probe the main assumptions of the approach.
Expanding, shearing and accelerating isotropic plane symmetric universe with conformal Kasner geometry
Authors: Hansraj Sudan , Govender Megandhren , Mewalal Narenee
Published: July 2018

We construct a model of a universe filled with a perfect fluid with isotropic particle pressure. The anisotropic plane symmetric Kasner spacetime is used as a seed metric and through a conformal mapping a perfect fluid is generated. The model is inhomogeneous, irrotational, shearing and accelerating. For negative time, the universe is expanding, while for positive time, it is collapsing. With the aid of graphical three-dimensional plots, it is established that the density and pressure hypersurfaces are smooth and singularity free. Additionally, the sound-speed index is computed and the fluid obeys the causality criterion. The non-static exact solution found may prove useful in the study of gravitational waves.
Search for light gauge boson Zμτ via tt̄h1 production at LHC
Authors: Hou Jin-Xin , Yue Chong-Xing , Guo Yu-Chen
Published: July 2018

We consider the production of the new gauge boson [Formula: see text] predicted by the [Formula: see text] model via SM-like Higgs boson [Formula: see text] decaying in the [Formula: see text] production at LHC. Considering the current constraints on the relevant free parameters, we calculate its production cross-section and further investigate the possibility of detecting [Formula: see text] through the process [Formula: see text] at LHC. We find that [Formula: see text] might be detected via this process at LHC with high integrated luminosity.
Formative Knowledge Assessment Through Games Using Concept Map and Game Theory
Authors: Kumar R.
Published: July 2018

This paper presents the results of a research work carried out in order to develop a novel gaming model for formative knowledge assessment (FKA). The benefits of FKA are well acknowledged by educators as it aids for better learning. As FKA is more frequent, the students’ willingness to take frequent assessments is poor. Often the students expressed that they felt uncomfortable and this resulted in lack of active participation. These are considered as major challenges to be addressed. Hence, this research aimed to find a way to make the assessments interesting. Based on the positive responses for game-based learning, a theoretical model that integrates the principles of extensive games (EGs) and concept map (CM), namely concept tree game (CTG), has been defined and developed. Further, a qualitative research has been performed to evaluate the impact of the FKA model. Major data gathering techniques used in this work were a class test, proposed FKA method, participant observation, survey and focussed team interview. The data were analysed statistically and the results revealed that it has been effective and useful for educators in assessing the knowledge of students and for students, it aided them to know where they stand and helped them to learn effectively.
Forecasting macro parameters representing the behavior of an applied multi-agent system
Authors: Kuravsky L. S. , Popkov S. I.
Published: July 2018

Under consideration is the method of probabilistic forecasting of macro parameters representing general patterns of an applied multi-agent system behavior. This system introduces the game interaction between a set of agents and a target. The behavior dynamics of such a system is described by a discrete-state discrete-time Markov random process and represented in the terms, which are considered convenient for interpretation and practical control of macro parameters. The corresponding calculations are based on common characteristics of the initial conditions. The probabilistic model of behavior of the system is generalized for the case of the mobile target and supplemented with formulas for the dynamical calculation of probabilistic distributions of the target’s defeat by the agents as well as the agent’s defeat by the target.
Shape from metric
Authors: Chern Albert , Knöppel Felix , Pinkall Ulrich , Schröder Peter
Published: July 2018

We study the
isometric immersion
problem for orientable surface triangle meshes endowed with only a metric: given the combinatorics of the mesh together with edge lengths, approximate an isometric immersion into R
3
. To address this challenge we develop a discrete theory for surface immersions into R
3
. It precisely characterizes a discrete immersion, up to subdivision and small perturbations. In particular our discrete theory correctly represents the topology of the space of immersions,
i.e.
, the regular homotopy classes which represent its connected components. Our approach relies on unit quaternions to represent triangle orientations and to encode, in their parallel transport, the topology of the immersion. In unison with this theory we develop a computational apparatus based on a variational principle. Minimizing a non-linear Dirichlet energy optimally finds extrinsic geometry for the given intrinsic geometry and ensures low metric approximation error.

We demonstrate our algorithm with a number of applications from mathematical visualization and art directed isometric shape deformation, which mimics the behavior of thin materials with high membrane stiffness.
Eulerian-on-lagrangian cloth simulation
Authors: Weidner Nicholas J. , Piddington Kyle , Levin David I. W. , Sueda Shinjiro
Published: July 2018

We resolve the longstanding problem of simulating the contact-mediated interaction of cloth and sharp geometric features by introducing an Eulerian-on-Lagrangian (EOL) approach to cloth simulation. Unlike traditional Lagrangian approaches to cloth simulation, our EOL approach permits bending exactly at and sliding over sharp edges, avoiding parasitic locking caused by over-constraining contact constraints. Wherever the cloth is in contact with sharp features, we insert EOL vertices into the cloth, while the rest of the cloth is simulated in the standard Lagrangian fashion. Our algorithm manifests as new equations of motion for EOL vertices, a contact-conforming remesher, and a set of simple constraint assignment rules, all of which can be incorporated into existing state-of-the-art cloth simulators to enable smooth, inequality-constrained contact between cloth and objects in the world.
A radiative transfer framework for spatially-correlated materials
Authors: Jarabo Adrian , Aliaga Carlos , Gutierrez Diego
Published: July 2018

We introduce a non-exponential radiative framework that takes into account the local spatial correlation of scattering particles in a medium. Most previous works in graphics have ignored this, assuming uncorrelated media with a uniform, random local distribution of particles. However, positive and negative correlation lead to slower- and faster-than-exponential attenuation respectively, which cannot be predicted by the Beer-Lambert law. As our results show, this has a major effect on extinction, and thus appearance. From recent advances in neutron transport, we first introduce our Extended Generalized Boltzmann Equation, and develop a general framework for light transport in correlated media. We lift the limitations of the original formulation, including an analysis of the boundary conditions, and present a model suitable for computer graphics, based on optical properties of the media and statistical distributions of scatterers. In addition, we present an analytic expression for transmittance in the case of positive correlation, and show how to incorporate it efficiently into a Monte Carlo renderer. We show results with a wide range of both positive and negative correlation, and demonstrate the differences compared to classic light transport.
Non-stationary texture synthesis by adversarial expansion
Authors: Zhou Yang , Zhu Zhen , Bai Xiang , Lischinski Dani , Cohen-Or Daniel , Huang Hui
Published: July 2018

The real world exhibits an abundance of non-stationary textures. Examples include textures with large scale structures, as well as spatially variant and inhomogeneous textures. While existing example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this paper, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle.
A moving least squares material point method with displacement discontinuity and two-way rigid body coupling
Authors: Hu Yuanming , Fang Yu , Ge Ziheng , Qu Ziyin , Zhu Yixin , Pradhana Andre , Jiang Chenfanfu
Published: July 2018

In this paper, we introduce the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM naturally leads to the formulation of Affine Particle-In-Cell (APIC) [Jiang et al. 2015] and Polynomial Particle-In-Cell [Fu et al. 2017] in a way that is consistent with a Galerkin-style weak form discretization of the governing equations. Additionally, it enables a new stress divergence discretization that effortlessly allows all MPM simulations to run two times faster than before. We also develop a Compatible Particle-In-Cell (CPIC) algorithm on top of MLS-MPM. Utilizing a colored distance field representation and a novel compatibility condition for particles and grid nodes, our framework enables the simulation of various new phenomena that are not previously supported by MPM, including material cutting, dynamic open boundaries, and two-way coupling with rigid bodies. MLS-MPM with CPIC is easy to implement and friendly to performance optimization.
Object-aware guidance for autonomous scene reconstruction
Authors: Liu Ligang , Xia Xi , Sun Han , Shen Qi , Xu Juzhan , Chen Bin , Huang Hui , Xu Kai
Published: July 2018

To carry out autonomous 3D scanning and online reconstruction of unknown indoor scenes, one has to find a balance between global exploration of the entire scene and local scanning of the objects within it. In this work, we propose a novel approach, which provides object-aware guidance for autoscanning, for exploring, reconstructing, and understanding an unknown scene within
one navigation pass.
Our approach interleaves between object analysis to identify the
next best object
(NBO) for global exploration, and object-aware information gain analysis to plan the
next best view
(NBV) for local scanning. First, an objectness-based segmentation method is introduced to extract semantic objects from the current scene surface via a multi-class graph cuts minimization. Then, an object of interest (OOI) is identified as the NBO which the robot aims to visit and scan. The robot then conducts fine scanning on the OOI with views determined by the NBV strategy. When the OOI is recognized as a full object, it can be replaced by its most similar 3D model in a shape database. The algorithm iterates until all of the objects are recognized and reconstructed in the scene. Various experiments and comparisons have shown the feasibility of our proposed approach.
Autocomplete 3D sculpting
Authors: Peng Mengqi , Xing Jun , Wei Li-Yi
Published: July 2018

Digital sculpting is a popular means to create 3D models but remains a challenging task. We propose a 3D sculpting system that assists users, especially novices, in freely creating models with reduced input labor and enhanced output quality. With an interactive sculpting interface, our system silently records and analyzes users' workflows including brush strokes and camera movements, and predicts what they might do in the future. Users can accept, partially accept, or ignore the suggestions and thus retain full control and individual style. They can also explicitly select and clone past workflows over output model regions. Our key idea is to consider
how
a model is authored via dynamic workflows in addition to
what
is shaped in static geometry. This allows our method for more accurate analysis of user intentions and more general synthesis of shape structures than prior workflow or geometry methods, such as large overlapping deformations. We evaluate our method via user feedbacks and authored models.
Robust solving of optical motion capture data by denoising
Authors: Holden Daniel
Published: July 2018

Raw optical motion capture data often includes errors such as occluded markers, mislabeled markers, and high frequency noise or jitter. Typically these errors must be fixed by hand - an extremely time-consuming and tedious task. Due to this, there is a large demand for tools or techniques which can alleviate this burden. In this research we present a tool that sidesteps this problem, and produces joint transforms directly from raw marker data (a task commonly called "solving") in a way that is extremely robust to errors in the input data using the machine learning technique of
denoising.
Starting with a set of marker configurations, and a large database of skeletal motion data such as the CMU motion capture database [CMU 2013b], we synthetically reconstruct marker locations using linear blend skinning and apply a unique
noise function
for corrupting this marker data - randomly removing and shifting markers to dynamically produce billions of examples of poses with errors similar to those found in real motion capture data. We then train a deep denoising feed-forward neural network to learn a mapping from this corrupted marker data to the corresponding transforms of the joints. Once trained, our neural network can be used as a replacement for the solving part of the motion capture pipeline, and, as it is very robust to errors, it completely removes the need for any manual clean-up of data. Our system is accurate enough to be used in production, generally achieving precision to within a few millimeters, while additionally being extremely fast to compute with low memory requirements.
Developability of triangle meshes
Authors: Stein Oded , Grinspun Eitan , Crane Keenan
Published: July 2018

Developable surfaces
are those that can be made by smoothly bending flat pieces without stretching or shearing. We introduce a definition of developability for triangle meshes which exactly captures two key properties of smooth developable surfaces, namely flattenability and presence of straight ruling lines. This definition provides a starting point for algorithms in developable surface modeling---we consider a variational approach that drives a given mesh toward developable pieces separated by regular seam curves. Computation amounts to gradient descent on an energy with support in the vertex star, without the need to explicitly cluster patches or identify seams. We briefly explore applications to developable design and manufacturing.
tempoGAN
Authors: Xie You , Franz Erik , Chu Mengyu , Thuerey Nils
Published: July 2018

We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. Our work represents a first approach to synthesize four-dimensional physics fields with neural networks. Based on a conditional generative adversarial network that is designed for the inference of three-dimensional volumetric data, our model generates consistent and detailed results by using a novel temporal discriminator, in addition to the commonly used spatial one. Our experiments show that the generator is able to infer more realistic high-resolution details by using additional physical quantities, such as low-resolution velocities or vorticities. Besides improvements in the training process and in the generated outputs, these inputs offer means for artistic control as well. We additionally employ a physics-aware data augmentation step, which is crucial to avoid overfitting and to reduce memory requirements. In this way, our network learns to generate adverted quantities with highly detailed, realistic, and temporally coherent features. Our method works instantaneously, using only a single time-step of low-resolution fluid data. We demonstrate the abilities of our method using a variety of complex inputs and applications in two and three dimensions.
Deep learning of biomimetic sensorimotor control for biomechanical human animation
Authors: Nakada Masaki , Zhou Tao , Chen Honglin , Weiss Tomer , Terzopoulos Demetri
Published: July 2018

We introduce a biomimetic framework for human sensorimotor control, which features a biomechanically simulated human musculoskeletal model actuated by numerous muscles, with eyes whose retinas have nonuniformly distributed photoreceptors. The virtual human's sensorimotor control system comprises 20 trained deep neural networks (DNNs), half constituting the neuromuscular motor subsystem, while the other half compose the visual sensory subsystem. Directly from the photoreceptor responses, 2 vision DNNs drive eye and head movements, while 8 vision DNNs extract visual information required to direct arm and leg actions. Ten DNNs achieve neuromuscular control---2 DNNs control the 216 neck muscles that actuate the cervicocephalic musculoskeletal complex to produce natural head movements, and 2 DNNs control each limb; i.e., the 29 muscles of each arm and 39 muscles of each leg. By synthesizing its own training data, our virtual human automatically learns efficient, online, active visuomotor control of its eyes, head, and limbs in order to perform nontrivial tasks involving the foveation and visual pursuit of target objects coupled with visually-guided limb-reaching actions to intercept the moving targets, as well as to carry out drawing and writing tasks.
An implicit frictional contact solver for adaptive cloth simulation
Authors: Li Jie , Daviet Gilles , Narain Rahul , Bertails-Descoubes Florence , Overby Matthew , Brown George E. , Boissieux Laurence
Published: July 2018

Cloth dynamics plays an important role in the visual appearance of moving characters. Properly accounting for contact and friction is of utmost importance to avoid cloth-body and cloth-cloth penetration and to capture typical folding and stick-slip behavior due to dry friction. We present here the first method able to account for cloth contact with exact Coulomb friction, treating both cloth self-contacts and contacts occurring between the cloth and an underlying character. Our key contribution is to observe that for a nodal system like cloth, the frictional contact problem may be formulated based on velocities as primary variables, without having to compute the costly Delassus operator. Then, by reversing the roles classically played by the velocities and the contact impulses, conical complementarity solvers of the literature can be adapted to solve for compatible velocities at nodes. To handle the full complexity of cloth dynamics scenarios, we have extended this base algorithm in two ways: first, towards the accurate treatment of frictional contact at any location of the cloth, through an adaptive node refinement strategy; second, towards the handling of multiple constraints at each node, through the duplication of constrained nodes and the adding of pin constraints between duplicata. Our method allows us to handle the complex cloth-cloth and cloth-body interactions in full-size garments with an unprecedented level of realism compared to former methods, while maintaining reasonable computational timings.
Toward wave-based sound synthesis for computer animation
Authors: Wang Jui-Hsien , Qu Ante , Langlois Timothy R. , James Doug L.
Published: July 2018

We explore an integrated approach to sound generation that supports a wide variety of physics-based simulation models and computer-animated phenomena. Targeting high-quality offline sound synthesis, we seek to resolve animation-driven sound radiation with near-field scattering and diffraction effects. The core of our approach is a sharp-interface finite-difference time-domain (FDTD) wavesolver, with a series of supporting algorithms to handle rapidly deforming and vibrating embedded interfaces arising in physics-based animation sound. Once the solver rasterizes these interfaces, it must evaluate acceleration boundary conditions (BCs) that involve model-and phenomena-specific computations. We introduce
acoustic shaders
as a mechanism to abstract away these complexities, and describe a variety of implementations for computer animation: near-rigid objects with ringing and acceleration noise, deformable (finite element) models such as thin shells, bubble-based water, and virtual characters. Since time-domain wave synthesis is expensive, we only simulate pressure waves in a small region about each sound source, then estimate a far-field pressure signal. To further improve scalability beyond multi-threading, we propose a fully
time-parallel sound synthesis
method that is demonstrated on commodity cloud computing resources. In addition to presenting results for multiple animation phenomena (water, rigid, shells, kinematic deformers, etc.) we also propose 3D automatic dialogue replacement (3DADR) for virtual characters so that pre-recorded dialogue can include character movement, and near-field shadowing and scattering sound effects.
Rule-free sewing pattern adjustment with precision and efficiency
Authors: Wang Huamin
Published: July 2018

Being able to customize sewing patterns for different human bodies without using any pre-defined adjustment rule will not only improve the realism of virtual humans in the entertainment industry, but also deeply affect the fashion industry by making fast fashion and made-to-measure garments more accessible. To meet the requirement set by the fashion industry, a sewing pattern adjustment system must be both efficient and precise, which unfortunately cannot be achieved by existing techniques. In this paper, we propose to solve sewing pattern adjustment as a nonlinear optimization problem immediately, rather than in two phases: a garment shape optimization phase and an inverse pattern design phase as in previous systems. This allows us to directly minimize the objective function that evaluates the fitting quality of the garment sewn from a pattern, without any compromise caused by the nonexistence of the solution to inverse pattern design. To improve the efficiency of our system, we carry out systematic research on a variety of optimization topics, including pattern parametrization, initialization, an inexact strategy, acceleration, and CPU-GPU implementation. We verify the usability of our system through automatic grading tests and made-to-measure tests. Designers and pattern makers confirm that our pattern results are able to preserve design details and their fitting qualities are acceptable. In our computational experiment, the system further demonstrates its efficiency, reliability, and flexibility of handling various pattern designs. While our current system still needs to overcome certain limitations, we believe it is a crucial step toward fully automatic pattern design and adjustment in the future.
Box cutter
Authors: Limper Max , Vining Nicholas , SHEFFER ALLA
Published: July 2018

Packed atlases, consisting of 2D parameterized charts, are ubiquitously used to store surface signals such as texture or normals. Tight packing is similarly used to arrange and cut-out 2D panels for fabrication from sheet materials.
Packing efficiency
, or the ratio between the areas of the packed atlas and its bounding box, significantly impacts downstream applications.

We propose
Box Cutter
, a new method for optimizing packing efficiency suitable for both settings. Our algorithm improves packing efficiency without changing distortion by strategically cutting and repacking the atlas charts or panels. It preserves the local mapping between the 3D surface and the atlas charts and retains global mapping continuity across the newly formed cuts. We balance packing efficiency improvement against increase in chart boundary length and enable users to directly control the acceptable amount of boundary elongation. While the problem we address is NP-hard, we provide an effective practical solution by iteratively detecting large rectangular empty spaces, or
void boxes
, in the current atlas packing and eliminating them by first refining the atlas using strategically placed axis-aligned cuts and then repacking the refined charts. We repeat this process until no further improvement is possible, or until the desired balance between packing improvement and boundary elongation is achieved. Packed chart atlases are only useful for the applications we address if their charts are overlap-free; yet many popular parameterization methods, used as-is, produce atlases with global overlaps. Our pre-processing step eliminates all input overlaps while explicitly minimizing the boundary length of the resulting overlap-free charts. We demonstrate our combined strategy on a large range of input atlases produced by diverse parameterization methods, as well as on multiple sets of 2D fabrication panels. Our framework dramatically improves the output packing efficiency on all inputs; for instance with boundary length increase capped at 50% we improve packing efficiency by 68% on average.
Water surface wavelets
Authors: Jeschke Stefan , Skřivan Tomáš , Müller-Fischer Matthias , Chentanez Nuttapong , Macklin Miles , Wojtan Chris
Published: July 2018

The current state of the art in real-time two-dimensional water wave simulation requires developers to choose between efficient Fourier-based methods, which lack interactions with moving obstacles, and finite-difference or finite element methods, which handle environmental interactions but are significantly more expensive. This paper attempts to bridge this long-standing gap between complexity and performance, by proposing a new wave simulation method that can faithfully simulate wave interactions with moving obstacles in real time while simultaneously preserving minute details and accommodating very large simulation domains.

Previous methods for simulating 2D water waves directly compute the change in height of the water surface, a strategy which imposes limitations based on the CFL condition (fast moving waves require small time steps) and Nyquist's limit (small wave details require closely-spaced simulation variables). This paper proposes a novel wavelet transformation that discretizes the liquid motion in terms of amplitude-like functions that vary over
space, frequency, and direction
, effectively generalizing Fourier-based methods to handle local interactions. Because these new variables change much more slowly over space than the original water height function, our change of variables drastically reduces the limitations of the CFL condition and Nyquist limit, allowing us to simulate highly detailed water waves at very large visual resolutions. Our discretization is amenable to fast summation and easy to parallelize. We also present basic extensions like pre-computed wave paths and two-way solid fluid coupling. Finally, we argue that our discretization provides a convenient set of variables for artistic manipulation, which we illustrate with a novel wave-painting interface.
3D printing spatially varying color and translucency
Authors: Brunton Alan , Arikan Can Ates , Tanksale Tejas Madan , Urban Philipp
Published: July 2018

We present an efficient and scalable pipeline for fabricating full-colored objects with spatially-varying translucency from practical and accessible input data via multi-material 3D printing. Observing that the costs associated with BSSRDF measurement and processing are high, the range of 3D printable BSSRDFs are severely limited, and that the human visual system relies only on simple high-level cues to perceive translucency, we propose a method based on reproducing perceptual translucency cues. The input to our pipeline is an RGBA signal defined on the surface of an object, making our approach accessible and practical for designers. We propose a framework for extending standard color management and profiling to combined color and translucency management using a gamut correspondence strategy we callopaque relative processing.We present an efficient streaming method to compute voxel-level material arrangements, achieving both realistic reproduction of measured translucent materials and artistic effects involving multiple fully or partially transparent geometries.
Scalable laplacian eigenfluids
Authors: Cui Qiaodong , Sen Pradeep , Kim Theodore
Published: July 2018

The Laplacian Eigenfunction method for fluid simulation, which we refer to as
Eigenfluids
, introduced an elegant new way to capture intricate fluid flows with near-zero viscosity. However, the approach does not scale well, as the memory cost grows prohibitively with the number of eigenfunctions. The method also lacks generality, because the dynamics are constrained to a closed box with Dirichlet boundaries, while open, Neumann boundaries are also needed in most practical scenarios. To address these limitations, we present a set of analytic eigenfunctions that supports uniform Neumann and Dirichlet conditions along each domain boundary, and show that by carefully applying the discrete sine and cosine transforms, the storage costs of the eigenfunctions can be made completely negligible. The resulting algorithm is both faster and more memory-efficient than previous approaches, and able to achieve lower viscosities than similar pseudo-spectral methods. We are able to surpass the scalability of the original Laplacian Eigenfunction approach by over two orders of magnitude when simulating rectangular domains. Finally, we show that the formulation allows forward scattering to be directed in a way that is not possible with any other method.
What characterizes personalities of graphic designs?
Authors: Zhao Nanxuan , Cao Ying , Lau Rynson W. H.
Published: July 2018

Graphic designers often manipulate the overall look and feel of their designs to convey certain personalities (e.g., cute, mysterious and romantic) to impress potential audiences and achieve business goals. However, understanding the factors that determine the personality of a design is challenging, as a graphic design is often a result of thousands of decisions on numerous factors, such as font, color, image, and layout. In this paper, we aim to answer the question of what characterizes the personality of a graphic design. To this end, we propose a deep learning framework for exploring the effects of various design factors on the perceived personalities of graphic designs. Our framework learns a convolutional neural network (called
personality scoring network
) to estimate the personality scores of graphic designs by ranking the crawled web data. Our personality scoring network automatically learns a visual representation that captures the semantics necessary to predict graphic design personality. With our personality scoring network, we systematically and quantitatively investigate how various design factors (e.g., color, font, and layout) affect design personality across different scales (from pixels, regions to elements). We also demonstrate a number of practical application scenarios of our network, including element-level design suggestion and example-based personality transfer.
Blended cured quasi-newton for distortion optimization
Authors: Zhu Yufeng , Bridson Robert , Kaufman Danny M.
Published: July 2018

Optimizing distortion energies over a mesh, in two or three dimensions, is a common and critical problem in physical simulation and geometry processing. We present three new improvements to the state of the art: a barrier-aware line-search filter that cures blocked descent steps due to element barrier terms and so enables rapid progress; an energy proxy model that adaptively blends the Sobolev (inverse-Laplacian-processed) gradient and L-BFGS descent to gain the advantages of both, while avoiding L-BFGS's current limitations in distortion optimization tasks; and a characteristic gradient norm providing a robust and largely mesh- and energy-independent convergence criterion that avoids wrongful termination when algorithms temporarily slow their progress. Together these improvements form the basis for Blended Cured Quasi-Newton (BCQN), a new distortion optimization algorithm. Over a wide range of problems over all scales we show that BCQN is generally the fastest and most robust method available, making some previously intractable problems practical while offering up to an order of magnitude improvement in others.
Gradient-domain volumetric photon density estimation
Authors: Gruson Adrien , Hua Binh-Son , Vibert Nicolas , Nowrouzezahrai Derek , Hachisuka Toshiya
Published: July 2018

Gradient-domain rendering can improve the convergence of surface-based light transport by exploiting smoothness in image space. Scenes with participating media exhibit similar smoothness and could potentially benefit from gradient-domain techniques. We introduce the first gradient-domain formulation of image synthesis with homogeneous participating media, including four novel and efficient gradient-domain volumetric density estimation algorithms. We show that naïve extensions of gradient domain path-space and density estimation methods to volumetric media, while functional, can result in inefficient estimators. Focussing on point-, beam- and plane-based gradient-domain estimators, we introduce a novel shift mapping that eliminates redundancies in the naïve formulations using spatial relaxation within the volume. We show that gradient-domain volumetric rendering improve convergence compared to primal domain state-of-the-art, across a suite of scenes. Our formulation and algorithms support progressive estimation and are easy to incorporate atop existing renderers.
Real-time data-driven interactive rough sketch inking
Authors: Simo-Serra Edgar , Iizuka Satoshi , Ishikawa Hiroshi
Published: July 2018

We present an interactive approach for
inking
, which is the process of turning a pencil rough sketch into a clean line drawing. The approach, which we call the
Smart Inker
, consists of several "smart" tools that intuitively react to user input, while guided by the input rough sketch, to efficiently and naturally connect lines, erase shading, and fine-tune the line drawing output. Our approach is data-driven: the tools are based on fully convolutional networks, which we train to exploit both the user edits and inaccurate rough sketch to produce accurate line drawings, allowing high-performance interactive editing in real-time on a variety of challenging rough sketch images. For the training of the tools, we developed two key techniques: one is the creation of training data by simulation of vague and quick user edits; the other is a line normalization based on learning from vector data. These techniques, in combination with our sketch-specific data augmentation, allow us to train the tools on heterogeneous data without actual user interaction. We validate our approach with an in-depth user study, comparing it with professional illustration software, and show that our approach is able to reduce inking time by a factor of 1.8X, while improving the results of amateur users.
Visual rhythm and beat
Authors: Davis Abe , Agrawala Maneesh
Published: July 2018

We present a visual analogue for musical rhythm derived from an analysis of motion in video, and show that alignment of visual rhythm with its musical counterpart results in the appearance of dance. Central to our work is the concept of visual beats --- patterns of motion that can be shifted in time to control visual rhythm. By warping visual beats into alignment with musical beats, we can create or manipulate the appearance of dance in video. Using this approach we demonstrate a variety of retargeting applications that control musical synchronization of audio and video: we can change what song performers are dancing to, warp irregular motion into alignment with music so that it appears to be dancing, or search collections of video for moments of accidentally dance-like motion that can be used to synthesize musical performances.
Deep context-aware descreening and rescreening of halftone images
Authors: Kim Tae-Hoon , Park Sang Il
Published: July 2018

A fully automatic method for descreening halftone images is presented based on convolutional neural networks with end-to-end learning. Incorporating context level information, the proposed method not only removes halftone artifacts but also synthesizes the fine details lost during halftone. The method consists of two main stages. In the first stage, intrinsic features of the scene are extracted, the low-frequency reconstruction of the image is estimated, and halftone patterns are removed. For the intrinsic features, the edges and object-categories are estimated and fed to the next stage as strong visual and contextual cues. In the second stage, fine details are synthesized on top of the low-frequency output based on an adversarial generative model. In addition, the novel problem of rescreening is addressed, where a natural input image is halftoned so as to be similar to a separately given reference halftone image. To this end, a two-stage convolutional neural network is also presented. Both networks are trained with millions of before-and-after example image pairs of various halftone styles. Qualitative and quantitative evaluations are provided, which demonstrates the effectiveness of the proposed methods.
Single-image SVBRDF capture with a rendering-aware deep network
Authors: Deschaintre Valentin , Aittala Miika , Durand Fredo , Drettakis George , Bousseau Adrien
Published: July 2018

Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, diffuse albedo, specular albedo and specular roughness from a single picture of a flat surface lit by a hand-held flash. We achieve this goal by introducing several innovations on training data acquisition and network design. For training, we leverage a large dataset of artist-created, procedural SVBRDFs which we sample and render under multiple lighting directions. We further amplify the data by material mixing to cover a wide diversity of shading effects, which allows our network to work across many material classes. Motivated by the observation that distant regions of a material sample often offer complementary visual cues, we design a network that combines an encoder-decoder convolutional track for local feature extraction with a fully-connected track for
global feature
extraction and propagation. Many important material effects are view-dependent, and as such ambiguous when observed in a single image. We tackle this challenge by defining the loss as a differentiable SVBRDF similarity metric that compares the
renderings
of the predicted maps against renderings of the ground truth from several lighting and viewing directions. Combined together, these novel ingredients bring clear improvement over state of the art methods for single-shot capture of spatially varying BRDFs.
Hyper-reduced projective dynamics
Authors: Brandt Christopher , Eisemann Elmar , Hildebrandt Klaus
Published: July 2018

We present a method for the real-time simulation of deformable objects that combines the robustness, generality, and high performance of Projective Dynamics with the efficiency and scalability offered by model reduction techniques. The method decouples the cost for time integration from the mesh resolution and can simulate large meshes in real-time. The proposed hyper-reduction of Projective Dynamics combines a novel fast approximation method for constraint projections and a scalable construction of sparse subspace bases. The resulting system achieves real-time rates for large sub-spaces enabling rich dynamics and can resolve general user interactions, collision constraints, external forces and changes to the materials. The construction of the hyper-reduced system does not require user-interaction and refrains from using training data or modal analysis, which results in a fast preprocessing stage.
Denoising with kernel prediction and asymmetric loss functions
Authors: Vogels Thijs , Rousselle Fabrice , Mcwilliams Brian , Röthlin Gerhard , Harvill Alex , Adler David , Meyer Mark , Novák Jan
Published: July 2018

We present a modular convolutional architecture for denoising rendered images. We expand on the capabilities of kernel-predicting networks by combining them with a number of task-specific modules, and optimizing the assembly using an asymmetric loss. The source-aware encoder---the first module in the assembly---extracts low-level features and embeds them into a common feature space, enabling quick adaptation of a trained network to novel data. The spatial and temporal modules extract abstract, high-level features for kernel-based reconstruction, which is performed at three different spatial scales to reduce low-frequency artifacts. The complete network is trained using a class of asymmetric loss functions that are designed to preserve details and provide the user with a direct control over the variance-bias trade-off during inference. We also propose an error-predicting module for inferring reconstruction error maps that can be used to drive adaptive sampling. Finally, we present a theoretical analysis of convergence rates of kernel-predicting architectures, shedding light on why kernel prediction performs better than synthesizing the colors directly, complementing the empirical evidence presented in this and previous works. We demonstrate that our networks attain results that compare favorably to state-of-the-art methods in terms of detail preservation, low-frequency noise removal, and temporal stability on a variety of production and academic datasets.
Optimizing for aesthetically pleasing quadrotor camera motion
Authors: Gebhardt Christoph , Stevšić Stefan , Hilliges Otmar
Published: July 2018

In this paper we first contribute a large scale online study (
N
≈ 400) to better understand aesthetic perception of aerial video. The results indicate that it is paramount to optimize smoothness of trajectories across all keyframes. However, for experts timing control remains an essential tool. Satisfying this dual goal is technically challenging because it requires giving up desirable properties in the optimization formulation. Second, informed by this study we propose a method that optimizes positional and temporal reference fit
jointly.
This allows to generate globally smooth trajectories, while retaining user control over reference timings. The formulation is posed as a variable, infinite horizon, contour-following algorithm. Finally, a comparative lab study indicates that our optimization scheme outperforms the state-of-the-art in terms of perceived usability and preference of resulting videos. For novices our method produces smoother and better looking results and also experts benefit from generated timings.