bluuebunny/crossref_metadata_2025 huggingface dataset:
Example-based expressive animation of 2D rigid bodies
Authors: Dvorožňák Marek , Bénard Pierre , Barla Pascal , Wang Oliver , Sýkora Daniel
Published: July 2017

We present a novel approach to facilitate the creation of stylized 2D rigid body animations. Our approach can handle multiple rigid objects following complex physically-simulated trajectories with collisions, while retaining a unique artistic style directly specified by the user. Starting with an existing target animation (e.g., produced by a physical simulation engine) an artist interactively draws over a sparse set of frames, and the desired appearance and motion stylization is automatically propagated to the rest of the sequence. The stylization process may also be performed in an off-line batch process from a small set of drawn sequences. To achieve these goals, we combine parametric deformation synthesis that generalizes and reuses hand-drawn exemplars, with non-parametric techniques that enhance the hand-drawn appearance of the synthesized sequence. We demonstrate the potential of our method on various complex rigid body animations which are created with an expressive hand-drawn look using notably less manual interventions as compared to traditional techniques.
Deformation-driven shape correspondence via shape recognition
Authors: Zhu Chenyang , Yi Renjiao , Lira Wallace , Alhashim Ibraheem , Xu Kai , Zhang Hao
Published: July 2017

Many approaches to shape comparison and recognition start by establishing a shape correspondence. We "turn the table" and show that quality shape correspondences can be obtained by performing many shape recognition tasks. What is more, the method we develop computes a
fine-grained, topology-varying
part correspondence between two 3D shapes where the core evaluation mechanism only recognizes shapes
globally.
This is made possible by casting the part correspondence problem in a deformation-driven framework and relying on a
data-driven
"deformation energy" which rates visual similarity between deformed shapes and models from a shape repository. Our basic premise is that if a correspondence between two chairs (or airplanes, bicycles, etc.) is correct, then a reasonable deformation between the two chairs anchored on the correspondence ought to produce
plausible
, "chair-like" in-between shapes.

Given two 3D shapes belonging to the same category, we perform a top-down, hierarchical search for part correspondences. For a candidate correspondence at each level of the search hierarchy, we deform one input shape into the other, while respecting the correspondence, and rate the correspondence based on how well the resulting deformed shapes resemble other shapes from ShapeNet belonging to the same category as the inputs. The resemblance, i.e., plausibility, is measured by comparing multi-view depth images over category-specific features learned for the various shape categories. We demonstrate clear improvements over state-of-the-art approaches through tests covering extensive sets of man-made models with rich geometric and topological variations.
Light field video capture using a learning-based hybrid imaging system
Authors: Wang Ting-Chun , Zhu Jun-Yan , Kalantari Nima Khademi , Efros Alexei A. , Ramamoorthi Ravi
Published: July 2017

Light field cameras have many advantages over traditional cameras, as they allow the user to change various camera settings
after
capture. However, capturing light fields requires a huge bandwidth to record the data: a modern light field camera can only take three images per second. This prevents current consumer light field cameras from capturing light field videos. Temporal interpolation at such extreme scale (10x, from 3 fps to 30 fps) is infeasible as too much information will be entirely missing between adjacent frames. Instead, we develop a hybrid imaging system, adding another standard video camera to capture the temporal information. Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps. We adopt a learning-based approach, which can be decomposed into two steps: spatio-temporal flow estimation and appearance estimation. The flow estimation propagates the angular information from the light field sequence to the 2D video, so we can warp input images to the target view. The appearance estimation then combines these warped images to output the final pixels. The whole process is trained end-to-end using convolutional neural networks. Experimental results demonstrate that our algorithm outperforms current video interpolation methods, enabling consumer light field videography, and making applications such as refocusing and parallax view generation achievable on videos for the first time.
Spherical orbifold tutte embeddings
Authors: Aigerman Noam , Kovalsky Shahar Z. , Lipman Yaron
Published: July 2017

This work presents an algorithm for injectively parameterizing surfaces into spherical target domains called spherical orbifolds. Spherical orbifolds are cone surfaces that are generated from symmetry groups of the sphere. The surface is mapped the spherical orbifold via an extension of Tutte's embedding. This embedding is proven to be bijective under mild additional assumptions, which hold in all experiments performed.

This work also completes the adaptation of Tutte's embedding to orbifolds of the three classic geometries - Euclidean, hyperbolic and spherical - where the first two were recently addressed.

The spherical orbifold embeddings approximate conformal maps and require relatively low computational times. The constant positive curvature of the spherical orbifolds, along with the flexibility of their cone angles, enables producing embeddings with lower isometric distortion compared to their Euclidean counterparts, a fact that makes spherical orbifolds a natural candidate for surface parameterization.
Geometric optimization via composite majorization
Authors: Shtengel Anna , Poranne Roi , Sorkine-Hornung Olga , Kovalsky Shahar Z. , Lipman Yaron
Published: July 2017

Many algorithms on meshes require the minimization of composite objectives,
i.e.
, energies that are compositions of simpler parts. Canonical examples include mesh parameterization and deformation. We propose a second order optimization approach that exploits this composite structure to efficiently converge to a local minimum.

Our main observation is that a convex-concave decomposition of the energy constituents is simple and readily available in many cases of practical relevance in graphics. We utilize such convex-concave decompositions to define a tight convex majorizer of the energy, which we employ as a
convex
second order approximation of the objective function. In contrast to existing approaches that largely use only local convexification, our method is able to take advantage of a more global view on the energy landscape. Our experiments on triangular meshes demonstrate that our approach outperforms the state of the art on standard problems in geometry processing, and potentially provide a unified framework for developing efficient geometric optimization algorithms.
Accommodation and comfort in head-mounted displays
Authors: Koulieris George-Alex , Bui Bee , Banks Martin S. , Drettakis George
Published: July 2017

Head-mounted displays (HMDs) often cause discomfort and even nausea. Improving comfort is therefore one of the most significant challenges for the design of such systems. In this paper, we evaluate the effect of different HMD display configurations on discomfort. We do this by designing a device to measure human visual behavior and evaluate viewer comfort. In particular, we focus on one known source of discomfort: the vergence-accommodation (VA) conflict. The VA conflict is the difference between accommodative and vergence response. In HMDs the eyes accommodate to a fixed screen distance while they converge to the simulated distance of the object of interest, requiring the viewer to undo the neural coupling between the two responses. Several methods have been proposed to alleviate the VA conflict, including Depth-of-Field (DoF) rendering, focus-adjustable lenses, and monovision. However, no previous work has investigated whether these solutions actually drive accommodation to the distance of the simulated object. If they did, the VA conflict would disappear, and we expect comfort to improve. We design the first device that allows us to measure accommodation in HMDs, and we use it to obtain accommodation measurements and to conduct a discomfort study. The results of the first experiment demonstrate that only the focus-adjustable-lens design drives accommodation effectively, while other solutions do not drive accommodation to the simulated distance and thus do not resolve the VA conflict. The second experiment measures discomfort. The results validate that the focus-adjustable-lens design improves comfort significantly more than the other solutions.
Anisotropic elastoplasticity for cloth, knit and hair frictional contact
Authors: Jiang Chenfanfu , Gast Theodore , Teran Joseph
Published: July 2017

The typical elastic surface or curve simulation method takes a Lagrangian approach and consists of three components: time integration, collision detection and collision response. The Lagrangian view is beneficial because it naturally allows for tracking of the codimensional manifold, however collision must then be detected and resolved separately. Eulerian methods are promising alternatives because collision processing is automatic and while this is effective for volumetric objects, advection of a codimensional manifold is too inaccurate in practice. We propose a novel hybrid Lagrangian/Eulerian approach that preserves the best aspects of both views. Similar to the Drucker-Prager and Mohr-Coulomb models for granular materials, we define our collision response with a novel elastoplastic constitutive model. To achieve this, we design an anisotropic hyperelastic constitutive model that separately characterizes the response to manifold strain as well as shearing and compression in the directions orthogonal to the manifold. We discretize the model with the Material Point Method and a novel codimensional Lagrangian/Eulerian update of the deformation gradient. Collision intensive scenarios with millions of degrees of freedom require only a few minutes per frame and examples with up to one million degrees of freedom run in less than thirty seconds per frame.
Field-aligned online surface reconstruction
Authors: Schertler Nico , Tarini Marco , Jakob Wenzel , Kazhdan Misha , Gumhold Stefan , Panozzo Daniele
Published: July 2017

Today's 3D scanning pipelines can be classified into two overarching categories: offline, high accuracy methods that rely on global optimization to reconstruct complex scenes with hundreds of millions of samples, and online methods that produce real-time but low-quality output, usually from structure-from-motion or depth sensors. The method proposed in this paper is the first to combine the benefits of both approaches, supporting online reconstruction of scenes with hundreds of millions of samples from high-resolution sensing modalities such as structured light or laser scanners. The key property of our algorithm is that it sidesteps the signed-distance computation of classical reconstruction techniques in favor of direct filtering, parametrization, and mesh and texture extraction. All of these steps can be realized using only weak notions of spatial neighborhoods, which allows for an implementation that scales approximately linearly with the size of each dataset that is integrated into a partial reconstruction. Combined, these algorithmic differences enable a drastically more efficient output-driven interactive scanning and reconstruction workflow, where the user is able to see the final quality field-aligned textured mesh during the entirety of the scanning procedure. Holes or parts with registration problems are displayed in real-time to the user and can be easily resolved by adding further localized scans, or by adjusting the input point cloud using our interactive editing tools with immediate visual feedback on the output mesh. We demonstrate the effectiveness of our algorithm in conjunction with a state-of-the-art structured light scanner and optical tracking system and test it on a large variety of challenging models.
GRASS
Authors: Li Jun , Xu Kai , Chaudhuri Siddhartha , Yumer Ersin , Zhang Hao , Guibas Leonidas
Published: July 2017

We introduce a novel neural network architecture for
encoding
and
synthesis
of 3D shapes, particularly their
structures.
Our key insight is that 3D shapes are effectively characterized by their
hierarchical
organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a
recursive
neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.
Similarity maps and field-guided T-splines
Authors: Campen Marcel , Zorin Denis
Published: July 2017

A variety of techniques were proposed to model smooth surfaces based on tensor product splines (e.g. subdivision surfaces, free-form splines, T-splines). Conversion of an input surface into such a representation is commonly achieved by constructing a global seamless parametrization, possibly aligned to a guiding cross-field (e.g. of principal curvature directions), and using this parametrization as domain to construct the spline-based surface.

One major fundamental difficulty in designing robust algorithms for this task is the fact that for common types, e.g. subdivision surfaces (requiring a conforming domain mesh) or T-spline surfaces (requiring a globally consistent knot interval assignment) reliably obtaining a suitable parametrization that has the same topological structure as the guiding field poses a major challenge. Even worse, not all fields do admit suitable parametrizations, and no concise conditions are known as to which fields do.

We present a class of surface constructions (T-splines with
halfedge knots
) and a class of parametrizations (
seamless similarity maps
) that are, in a sense, a perfect match for the task: for
any
given guiding field structure, a compatible parametrization of this kind exists and a smooth piecewise rational surface with exactly the same structure as the input field can be constructed from it. As a byproduct, this enables full control over extraordinary points. The construction is backward compatible with classical NURBS. We present efficient algorithms for building discrete conformal similarity maps and associated T-meshes and T-spline surfaces.
Constrained palette-space exploration
Authors: Mellado Nicolas , Vanderhaeghe David , Hoarau Charlotte , Christophe Sidonie , Brédif Mathieu , Barthe Loic
Published: July 2017

Color palettes are widely used by artists to define colors of artworks and explore color designs. In general, artists select the colors of a palette by following a set of rules, e.g. contrast or relative luminance. Existing interactive palette exploration tools explore palette spaces following limited constraints defined as geometric configurations in color space e.g. harmony rules on the color wheel. Palette search algorithms sample palettes from color relations learned from an input dataset, however they cannot provide interactive user edits and palette refinement.

We introduce in this work a new versatile formulation enabling the creation of constraint-based interactive palette exploration systems. Our technical contribution is a graph-based palette representation, from which we define palette exploration as a minimization problem that can be solved efficiently and provide real-time feedback. Based on our formulation, we introduce two interactive palette exploration strategies: constrained palette exploration, and for the first time, constrained palette interpolation. We demonstrate the performances of our approach on various application cases and evaluate how it helps users finding trade-offs between concurrent constraints.
Multi-species simulation of porous sand and water mixtures
Authors: Tampubolon Andre Pradhana , Gast Theodore , Klár Gergely , Fu Chuyuan , Teran Joseph , Jiang Chenfanfu , Museth Ken
Published: July 2017

We present a multi-species model for the simulation of gravity driven landslides and debris flows with porous sand and water interactions. We use continuum mixture theory to describe individual phases where each species individually obeys conservation of mass and momentum and they are coupled through a momentum exchange term. Water is modeled as a weakly compressible fluid and sand is modeled with an elastoplastic law whose cohesion varies with water saturation. We use a two-grid Material Point Method to discretize the governing equations. The momentum exchange term in the mixture theory is relatively stiff and we use semi-implicit time stepping to avoid associated small time steps. Our semi-implicit treatment is explicit in plasticity and preserves symmetry of force linearizations. We develop a novel regularization of the elastic part of the sand constitutive model that better mimics plasticity during the implicit solve to prevent numerical cohesion artifacts that would otherwise have occurred. Lastly, we develop an improved return mapping for sand plasticity that prevents volume gain artifacts in the traditional Drucker-Prager model.
Botanical materials based on biomechanics
Authors: Wang Bohan , Zhao Yili , Barbič Jernej
Published: July 2017

Botanical simulation plays an important role in many fields including visual effects, games and virtual reality. Previous plant simulation research has focused on computing physically based motion, under the assumption that the material properties are known. It is too tedious and impractical to manually set the spatially-varying material properties of complex trees. In this paper, we give a method to set the mass density, stiffness and damping properties of individual tree components (branches and leaves) using a small number of intuitive parameters. Our method is rooted in plant biomechanics literature and builds upon power laws observed in real botanical systems. We demonstrate our materials by simulating them using offline and model-reduced FEM simulators. Our parameters can be tuned directly by artists; but we also give a technique to infer the parameters from ground truth videos of real trees. Our materials produce tree animations that look much more similar to real trees than previous methods, as evidenced by our user study and experiments.
Nautilus
Authors: Lukáč Michal , Sýkora Daniel , Sunkavalli Kalyan , Shechtman Eli , Jamriška Ondřej , Carr Nathan , Pajdla Tomáš
Published: July 2017

Natural images often exhibit symmetries that should be taken into account when editing them. In this paper we present
Nautilus
--- a method for automatically identifying symmetric regions in an image along with their corresponding symmetry transformations. We compute dense local similarity symmetry transformations using a novel variant of the Generalised PatchMatch algorithm that uses Metropolis-Hastings sampling. We combine and refine these local symmetries using an extended Lucas-Kanade algorithm to compute regional transformations and their spatial extents. Our approach produces dense estimates of complex symmetries that are combinations of translation, rotation, scale, and reflection under perspective distortion. This enables a number of automatic symmetry-aware image editing applications including inpainting, rectification, beautification, and segmentation, and we demonstrate state-of-the-art applications for each of them.
Robust eXtended finite elements for complex cutting of deformables
Authors: Koschier Dan , Bender Jan , Thuerey Nils
Published: July 2017

In this paper we present a robust remeshing-free cutting algorithm on the basis of the eXtended Finite Element Method (XFEM) and fully implicit time integration. One of the most crucial points of the XFEM is that integrals over discontinuous polynomials have to be computed on subdomains of the polyhedral elements. Most existing approaches construct a cut-aligned auxiliary mesh for integration. In contrast, we propose a cutting algorithm that includes the construction of specialized quadrature rules for each dissected element without the requirement to explicitly represent the arising subdomains. Moreover, we solve the problem of ill-conditioned or even numerically singular solver matrices during time integration using a novel algorithm that constrains non-contributing degrees of freedom (DOFs) and introduce a preconditioner that efficiently reuses the constructed quadrature weights.

Our method is particularly suitable for fine structural cutting as it decouples the added number of DOFs from the cut's geometry and correctly preserves geometry and physical properties by accurate integration. Due to the implicit time integration these fine features can still be simulated robustly using large time steps. As opposed to this, the vast majority of existing approaches either use remeshing or element duplication. Remeshing based methods are able to correctly preserve physical quantities but strongly couple cut geometry and mesh resolution leading to an unnecessary large number of additional DOFs. Element duplication based approaches keep the number of additional DOFs small but fail at correct conservation of mass and stiffness properties. We verify consistency and robustness of our approach on simple and reproducible academic examples while stability and applicability are demonstrated in large scenarios with complex and fine structural cutting.
Variance-minimizing transport plans for inter-surface mapping
Authors: Mandad Manish , Cohen-Steiner David , Kobbelt Leif , Alliez Pierre , Desbrun Mathieu
Published: July 2017

We introduce an efficient computational method for generating dense and low distortion maps between two arbitrary surfaces of same genus. Instead of relying on semantic correspondences or surface parameterization, we directly optimize a variance-minimizing transport plan between two input surfaces that defines an as-conformal-as-possible inter-surface map satisfying a user-prescribed bound on area distortion. The transport plan is computed via two alternating convex optimizations, and is shown to minimize a generalized Dirichlet energy of both the map and its inverse. Computational efficiency is achieved through a coarse-to-fine approach in diffusion geometry, with Sinkhorn iterations modified to enforce bounded area distortion. The resulting inter-surface mapping algorithm applies to arbitrary shapes robustly, with little to no user interaction.
Spatiotemporal atlas parameterization for evolving meshes
Authors: Prada Fabián , Kazhdan Misha , Chuang Ming , Collet Alvaro , Hoppe Hugues
Published: July 2017

We convert a sequence of unstructured textured meshes into a mesh with incrementally changing connectivity and atlas parameterization. Like prior work on surface tracking, we seek temporally coherent mesh connectivity to enable efficient representation of surface geometry and texture. Like recent work on evolving meshes, we pursue local remeshing to permit tracking over long sequences containing significant deformations or topological changes. Our main contribution is to show that both goals are realizable within a common framework that simultaneously evolves both the set of mesh triangles and the parametric map. Sparsifying the remeshing operations allows the formation of large spatiotemporal texture charts. These charts are packed as prisms into a 3D atlas for a texture video. Reducing tracking drift using mesh-based optical flow helps improve compression of the resulting video stream.
A forward scattering dipole model from a functional integral approximation
Authors: Frederickx Roald , Dutré Philip
Published: July 2017

Rendering translucent materials with physically based Monte Carlo methods tends to be computationally expensive due to the long chains of volumetric scattering interactions. In the case of strongly forward scattering materials, the problem gets compounded since each scattering interaction becomes highly anisotropic and near-specular. Various well-known approaches try to avoid the resulting sampling problem through analytical approximations based on diffusion theory. Although these methods are computationally efficient, their assumption of diffusive, isotropic scattering can lead to considerable errors when rendering forward scattering materials, even in the optically dense limit. In this paper, we present an analytical subsurface scattering model, derived with the explicit assumption of strong forward scattering. Our model is not based on diffusion theory, but follows from a connection that we identified between the functional integral formulation of radiative transport and the partition function of a worm-like chain in polymer physics. Our resulting model does not need a separate Monte Carlo solution for unscattered or single-scattered contributions, nor does it require ad-hoc regularization procedures. It has a single singularity by design, corresponding to the initial unscattered propagation, which can be accounted for by the extensive analytical importance sampling scheme that we provide. Our model captures the full behaviour of forward scattering media, ranging from unscattered straight-line propagation to the fully diffusive limit. Moreover, we derive a novel forward scattering BRDF as limiting case of our subsurface scattering model, which can be used in a level of detail hierarchy. We show how our model can be integrated in existing Monte Carlo rendering algorithms, and make comparisons to previous approaches.
Image-based reconstruction of wire art
Authors: Liu Lingjie , Ceylan Duygu , Lin Cheng , Wang Wenping , Mitra Niloy J.
Published: July 2017

Objects created by connecting and bending wires are common in furniture design, metal sculpting, wire jewelry, etc. Reconstructing such objects with traditional depth and image based methods is extremely difficult due to their unique characteristics such as lack of features, thin elements, and severe self-occlusions. We present a novel image-based method that reconstructs a set of continuous 3D wires used to create such an object, where each wire is composed of an ordered set of 3D curve segments. Our method exploits two main observations:
simplicity
- wire objects are often created using only a small number of wires, and
smoothness
- each wire is primarily smoothly bent with sharp features appearing only at joints or isolated points. In light of these observations, we tackle the challenging image correspondence problem across featureless wires by first generating multiple candidate 3D curve segments and then solving a global selection problem that balances between image and smoothness cues to identify the correct 3D curves. Next, we recover a decomposition of such curves into a set of distinct and continuous wires by formulating a
multiple traveling salesman problem
, which finds smooth paths,
i.e.
, wires, connecting the curves. We demonstrate our method on a wide set of real examples with varying complexity and present high-fidelity results using only 3 images for each object. We provide the source code and data for our work in the project website.
Data-driven physics for human soft tissue animation
Authors: Kim Meekyoung , Pons-Moll Gerard , Pujades Sergi , Bang Seungbae , Kim Jinwook , Black Michael J. , Lee Sung-Hee
Published: July 2017

Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.
Epipolar time-of-flight imaging
Authors: Achar Supreeth , Bartels Joseph R. , Whittaker William L. 'Red' , Kutulakos Kiriakos N. , Narasimhan Srinivasa G.
Published: July 2017

Consumer time-of-flight depth cameras like Kinect and PMD are cheap, compact and produce video-rate depth maps in short-range applications. In this paper we apply energy-efficient epipolar imaging to the ToF domain to significantly expand the versatility of these sensors: we demonstrate live 3D imaging at over 15 m range outdoors in bright sunlight; robustness to global transport effects such as specular and diffuse inter-reflections---the first live demonstration for this ToF technology; interference-free 3D imaging in the presence of many ToF sensors, even when they are all operating at the same optical wavelength and modulation frequency; and blur-free, distortion-free 3D video in the presence of severe camera shake. We believe these achievements can make such cheap ToF devices broadly applicable in consumer and robotics domains.
A spherical cap preserving parameterization for spherical distributions
Authors: Dupuy Jonathan , Heitz Eric , Belcour Laurent
Published: July 2017

We introduce a novel parameterization for spherical distributions that is based on a point located inside the sphere, which we call a pivot. The pivot serves as the center of a straight-line projection that maps solid angles onto the opposite side of the sphere. By transforming spherical distributions in this way, we derive novel parametric spherical distributions that can be evaluated and importance-sampled from the original distributions using simple, closed-form expressions. Moreover, we prove that if the original distribution can be sampled and/or integrated over a spherical cap, then so can the transformed distribution. We exploit the properties of our parameterization to derive efficient spherical lighting techniques for both real-time and offline rendering. Our techniques are robust, fast, easy to implement, and achieve quality that is superior to previous work.
Consistent functional cross field design for mesh quadrangulation
Authors: Azencot Omri , Corman Etienne , Ben-Chen Mirela , Ovsjanikov Maks
Published: July 2017

We propose a novel technique for computing consistent cross fields on a pair of triangle meshes given an input correspondence, which we use as guiding fields for approximately consistent quadrangulations. Unlike the majority of existing methods our approach does not assume that the meshes share the same connectivity or even have the same number of vertices, and furthermore does not place any restrictions on the topology (genus) of the shapes. Importantly, our method is robust with respect to small perturbations of the given correspondence, as it only relies on the transportation of real-valued functions and thus avoids the costly and error-prone estimation of the map differential. Key to this robustness is a novel formulation, which relies on the previously-proposed notion of
power vectors
, and we show how consistency can be enforced without pre-alignment of local basis frames, in which these power vectors are computed. We demonstrate that using the same formulation we can both compute a quadrangulation that would respect a given
symmetry
on the same shape or a map across a pair of shapes. We provide quantitative and qualitative comparison of our method with several baselines and show that it both provides more accurate results and allows to handle more general cases than existing techniques.
Interactive design of animated plushies
Authors: Bern James M. , Chang Kai-Hung , Coros Stelian
Published: July 2017

We present a computational approach to creating animated plushies, soft robotic plush toys specifically-designed to reenact user-authored motions. Our design process is inspired by muscular hydrostat structures, which drive highly versatile motions in many biological systems. We begin by instrumenting simulated plush toys with a large number of small, independently-actuated, virtual muscle-fibers. Through an intuitive posing interface, users then begin animating their plushie. A novel numerical solver, reminiscent of inverse-kinematics, computes optimal contractions for each muscle-fiber such that the soft body of the plushie deforms to best match user input. By analyzing the co-activation patterns of the fibers that contribute most to the plushie's motions, our design system generates physically-realizable winch-tendon networks. Winch-tendon networks model the motorized cable-driven actuation mechanisms that drive the motions of our real-life plush toy prototypes. We demonstrate the effectiveness of our computational approach by co-designing motions and actuation systems for a variety of physically-simulated and fabricated plushies.
Printing anisotropic appearance with magnetic flakes
Authors: Pereira Thiago , Leme Carolina L. A. Paes , Marschner Steve , Rusinkiewicz Szymon
Published: July 2017

The ability to fabricate surfaces with fine control over bidirectional reflectance (BRDF) is a long-standing goal in appearance research, with applications in product design and manufacturing. We propose a technique that embeds magnetic flakes in a photo-cured resin, allowing the orientation distribution of those flakes to be controlled at printing time using a magnetic field. We show that time-varying magnetic fields allow us to control off-specular lobe direction, anisotropy, and lobe width, while using multiple spatial masks displayed by a UV projector allows for spatial variation. We demonstrate optical effects including bump maps: fat surfaces with spatially-varying specular lobe direction.
Real-time user-guided image colorization with learned deep priors
Authors: Zhang Richard , Zhu Jun-Yan , Isola Phillip , Geng Xinyang , Lin Angela S. , Yu Tianhe , Efros Alexei A.
Published: July 2017

We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN). Rather than using hand-defined rules, the network propagates user edits by fusing low-level cues along with high-level semantic information,
learned from large-scale data.
We train on a million images, with simulated user inputs. To guide the user towards efficient input selection, the system recommends likely colors based on the input image and current user inputs. The colorization is performed in a single feed-forward pass, enabling real-time use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and offers large improvements in colorization quality with just a minute of use. In addition, we demonstrate that the framework can incorporate other user "hints" to the desired colorization, showing an application to color histogram transfer.
Implicit crowds
Authors: Karamouzas Ioannis , Sohre Nick , Narain Rahul , Guy Stephen J.
Published: July 2017

Large multi-agent systems such as crowds involve inter-agent interactions that are typically anticipatory in nature, depending strongly on both the positions and the velocities of agents. We show how the nonlinear, anticipatory forces seen in multi-agent systems can be made compatible with recent work on energy-based formulations in physics-based animation, and propose a simple and effective optimization-based integration scheme for implicit integration of such systems. We apply this approach to crowd simulation by using a state-of-the-art model derived from a recent analysis of human crowd data, and adapting it to our framework. Our approach provides, for the first time, guaranteed collision-free motion while simultaneously maintaining high-quality collective behavior in a way that is insensitive to simulation parameters such as time step size and crowd density. These benefits are demonstrated through simulation results on various challenging scenarios and validation against real-world crowd data.
CurveUps
Authors: Guseinov Ruslan , Miguel Eder , Bickel Bernd
Published: July 2017

We present a computational approach for designing CurveUps, curvy shells that form from an initially flat state. They consist of small rigid tiles that are tightly held together by two pre-stretched elastic sheets attached to them. Our method allows the realization of smooth, doubly curved surfaces that can be fabricated as a flat piece. Once released, the restoring forces of the pre-stretched sheets support the object to take shape in 3D. CurveUps are structurally stable in their target configuration. The design process starts with a target surface. Our method generates a tile layout in 2D and optimizes the distribution, shape, and attachment areas of the tiles to obtain a configuration that is fabricable and in which the curved up state closely matches the target. Our approach is based on an efficient approximate model and a local optimization strategy for an otherwise intractable nonlinear optimization problem. We demonstrate the effectiveness of our approach for a wide range of shapes, all realized as physical prototypes.
ClothCap
Authors: Pons-Moll Gerard , Pujades Sergi , Hu Sonny , Black Michael J.
Published: July 2017

Designing and simulating realistic clothing is challenging. Previous methods addressing the capture of clothing from 3D scans have been limited to single garments and simple motions, lack detail, or require specialized texture patterns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear multiple pieces of clothing at a time. To estimate the shape of such clothing, track it over time, and render it believably, each garment must be segmented from the others and the body. Our
ClothCap
approach uses a new multi-part 3D model of clothed bodies, automatically segments each piece of clothing, estimates the minimally clothed body shape and pose under the clothing, and tracks the 3D deformations of the clothing over time. We estimate the garments and their motion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps. ClothCap is able to capture a clothed person in motion, extract their clothing, and retarget the clothing to new body shapes; this provides a step towards virtual try-on.
Symmetry in Software Synthesis
Authors: Goens Andrés , Siccha Sergio , Castrillon Jeronimo
Published: July 2017

With the surge of multi- and many-core systems, much research has focused on algorithms for mapping and scheduling on these complex platforms. Large classes of these algorithms face scalability problems. This is why diverse methods are commonly used for reducing the search space. While most such approaches leverage the inherent symmetry of architectures and applications, they do it in a problem-specific and intuitive way. However, intuitive approaches become impractical with growing hardware complexity, like Network-on-Chip interconnect or heterogeneous cores. In this article, we present a formal framework that can determine the inherent local and global symmetry of architectures and applications algorithmically and leverage these for problems in software synthesis. Our approach is based on the mathematical theory of groups and a generalization called inverse semigroups. We evaluate our approach in two state-of-the-art mapping frameworks. Even for the platforms with a handful of cores of today and moderate-sized benchmarks, our approach consistently yields reductions of the overall execution time of algorithms. We obtain a speedup of more than 10 × for one use-case and saved 10% of time in another.
The Judicialization of Health Care: A Global South Perspective
Authors: Lamprea Everaldo
Published: July 2017

This article charts the trajectory of the judicialization of health care from the perspective of Global South countries. It shows how the emergence of health rights litigation in the 1990s and early 2000s was bolstered by the global expansion of the HIV/AIDS epidemic and by major constitutional reforms that triggered a period of rights revolutions in South Africa and several Latin American countries. This article also tracks the litigation epidemic in countries like Colombia and Brazil, where the escalation of health rights lawsuits is threatening the financial stability of health systems and the fair allocation of scarce health resources. It concludes by discussing a fundamental challenge confronting the field, namely, how to look upstream for new approaches to the right to health to reinstate litigation and adjudication as mechanisms to promote more equitable health systems.
Social Networks and Gang Violence Reduction
Authors: Sierra-Arévalo Michael , Papachristos Andrew V.
Published: July 2017

This review traces the origins, development, and use of social network analysis in gang research and gang violence reduction strategies. Although early gang scholars intuitively recognized the networked nature of gangs and gang violence, such insights were not always leveraged by gang violence reduction efforts that became increasingly enforcement-centric throughout the twentieth century. This review describes these historical shifts, the recent advent of social network analysis in research and gang interventions, and future directions that research and interventions can take to develop a more victim-focused approach to gang violence reduction.
Neuroprotection in Glaucoma: Animal Models and Clinical Trials
Authors: Almasieh Mohammadali , Levin Leonard A.
Published: July 2017

Glaucoma is a progressive neurodegenerative disease that frequently results in irreversible blindness. Glaucoma causes death of retinal ganglion cells (RGCs) and their axons in the optic nerve, resulting in visual field deficits and eventual loss of visual acuity. Glaucoma is a complex optic neuropathy, and a successful strategy for its treatment requires not only better management of known risk factors such as elevated intraocular pressure and the development of improved tools for detecting RGC injury but also treatments that address this injury (i.e., neuroprotection). Experimental models of glaucoma provide insight into the cellular and molecular mechanisms of glaucomatous optic neuropathy and aid the development of neuroprotective therapies.
Enhanced NO-dependent pulmonary vasodilation limits increased vasoconstrictor sensitivity in neonatal chronic hypoxia
Authors: Sheak Joshua R. , Weise-Cross Laura , deKay Ray J. , Walker Benjimen R. , Jernigan Nikki L. , Resta Thomas C.
Published: July 2017

Augmented vasoconstrictor reactivity is thought to play an important role in the development of chronic hypoxia (CH)-induced neonatal pulmonary hypertension. However, whether this response to CH results from pulmonary endothelial dysfunction and reduced nitric oxide (NO)-mediated vasodilation is not well understood. We hypothesized that neonatal CH enhances basal tone and pulmonary vasoconstrictor sensitivity by limiting NO-dependent pulmonary vasodilation. To test this hypothesis, we assessed the effects of the NO synthase (NOS) inhibitor Nω-nitro-l-arginine (l-NNA) on baseline pulmonary vascular resistance (PVR) and vasoconstrictor sensitivity to the thromboxane mimetic U-46619 in saline -perfused lungs (in situ) from 2-wk-old control and CH (12-day exposure, 0.5 atm) Sprague-Dawley rats. Basal tone was defined as that reversed by exogenous NO (spermine NONOate). CH neonates displayed elevated right ventricular systolic pressure (in vivo) and right ventricular hypertrophy, indicative of pulmonary hypertension. Perfused lungs from CH rats demonstrated greater baseline PVR, basal tone, and U-46619-mediated vasoconstriction compared with control rats in the absence of l-NNA. l-NNA markedly increased baseline PVR and reactivity to U-46619 in lungs from CH neonates, further augmenting vasoconstrictor sensitivity compared with control lungs. Exposure to CH also enhanced NO-dependent vasodilation to arginine vasopressin, pulmonary expression of NOS III [endothelial NOS (eNOS)], and eNOS phosphorylation at activation residue Ser1177. However, CH did not alter lung nitrotyrosine levels, a posttranslational modification reflecting [Formula: see text] scavenging of NO. We conclude that, in contrast to our hypothesis, enhanced basal tone and agonist-induced vasoconstriction after neonatal CH is limited by increased NO-dependent pulmonary vasodilation resulting from greater eNOS expression and phosphorylation at activation residue Ser1177.

NEW & NOTEWORTHY This research is the first to demonstrate enhanced nitric oxide-dependent vasodilation that limits increased vasoconstrictor reactivity in neonatal pulmonary hypertension. These results suggest that augmented vasoconstriction in this setting reflects changes in smooth muscle reactivity rather than a reduction in nitric oxide-dependent pulmonary vasodilation.