bluuebunny/crossref_metadata_2025 huggingface dataset:
To Transmit or Not to Transmit
Authors: Panagidi K. , Anagnostopoulos C. , Chalvatzaras A. , Hadjiefthymiades S.
Published: August 2020

The Mobile IoT domain has been significantly expanded with the proliferation of drones and unmanned robotic devices. In this new landscape, the communication between the resource-constrained device and the fixed infrastructure is similarly expanded to include new messages of varying importance, control, and monitoring. To efficiently and effectively control the exchange of such messages subject to the stochastic nature of the underlying wireless network, we design a time-optimized, dynamic, and distributed decision-making mechanism based on the principles of the Optimal Stopping and Change Detection theories. The findings from our experimentation platform are promising and solidly supportive to a vast spectrum of real-time and latency-sensitive applications with quality-of-service requirements in mobile IoT environments.
Analytic spherical harmonic gradients for real-time rendering with many polygonal area lights
Authors: Wu Lifan , Cai Guangyan , Zhao Shuang , Ramamoorthi Ravi
Published: August 2020

Recent work has developed analytic formulae for spherical harmonic (SH) coefficients from uniform polygonal lights, enabling near-field area lights to be included in Precomputed Radiance Transfer (PRT) systems, and in offline rendering. However, the method is inefficient since coefficients need to be recomputed at each vertex or shading point, for each light, even though the SH coefficients vary smoothly in space. The complexity scales linearly with the number of lights, making many-light rendering difficult. In this paper, we develop a
novel analytic formula for the spatial gradients of the spherical harmonic coefficients for uniform polygonal area lights.
The result is a significant generalization, involving the Reynolds transport theorem to reduce the problem to a boundary integral for which we derive a new analytic formula, showing how to reduce a key term to an earlier recurrence for SH coefficients. The implementation requires only minor additions to existing code for SH coefficients. The results also hold implications for recent efforts on differentiable rendering. We show that SH gradients enable very sparse spatial sampling, followed by accurate Hermite interpolation. This enables
scaling PRT to hundreds of area lights
with minimal overhead and real-time frame rates. Moreover, the SH gradient formula is a new mathematical result that potentially enables many other graphics applications.
Penrose
Authors: Ye Katherine , Ni Wode , Krieger Max , Ma'ayan Dor , Wise Jenna , Aldrich Jonathan , Sunshine Joshua , Crane Keenan
Published: August 2020

We introduce a system called Penrose for creating mathematical diagrams. Its basic functionality is to translate abstract statements written in familiar math-like notation into one or more possible visual representations. Rather than rely on a fixed library of visualization tools, the visual representation is user-defined in a constraint-based specification language; diagrams are then generated automatically via constrained numerical optimization. The system is user-extensible to many domains of mathematics, and is fast enough for iterative design exploration. In contrast to tools that specify diagrams via direct manipulation or low-level graphics programming, Penrose enables rapid creation and exploration of diagrams that faithfully preserve the underlying mathematical meaning. We demonstrate the effectiveness and generality of the system by showing how it can be used to illustrate a diverse set of concepts from mathematics and computer graphics.
Cut-enhanced PolyCube-maps for feature-aware all-hex meshing
Authors: Guo Hao-Xiang , Liu Xiaohan , Yan Dong-Ming , Liu Yang
Published: August 2020

Volumetric PolyCube-Map-based methods offer automatic ways to construct all-hexahedral meshes for closed 3D polyhedral domains, but their meshing quality is limited by the lack of interior singularities and feature alignment. In the presented work, we propose
cut-enhanced PolyCube-Maps
, to introduce essential interior singularities and preserve most input features. Our main idea is simple and intuitive: by inserting proper parameterization seams into the initial PolyCube-Map via novel PolyCube cutting operations, the mapping distortion can be reduced significantly.

The cut-enhanced PolyCube-Map computation includes feature-aware PolyCube-Map construction and cut-enhanced PolyCube deformation. The former aims to preserve input feature edges during the initial PolyCube-Map construction. The latter introduces seams into the volumetric PolyCube shape by cutting it through selective PolyCube edges and deforms the modified PolyCube under the seamless constraints to compute a low-distortion PolyCube-Map. The hexahedral mesh induced by the final PolyCube-Map can be further enhanced by our mesh improvement algorithm.

We validate the efficacy of our method on a collection of more than one hundred CAD models and demonstrate its advantages over other automatic all-hex meshing methods and padding strategies. The limitations of cut-enhanced PolyCube-Maps are also discussed thoroughly.
TilinGNN
Authors: Xu Hao , Hui Ka-Hei , Fu Chi-Wing , Zhang Hao
Published: August 2020

We introduce the first
neural optimization
framework to solve a classical instance of the
tiling
problem. Namely, we seek a
non-periodic
tiling of an arbitrary 2D shape using
one or more
types of tiles---the tiles
maximally
fill the shape's interior without overlaps or holes. To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges. Further, we build a
graph convolutional neural network
, coined TilinGNN, to progressively propagate and aggregate features over graph edges and predict tile placements. TilinGNN is trained by maximizing the tiling coverage on target shapes, while avoiding overlaps and holes between the tiles. Importantly, our network is
self-supervised
, as we articulate these criteria as loss terms defined on the network outputs, without the need of ground-truth tiling solutions. After training, the runtime of TilinGNN is roughly
linear
to the number of candidate tile locations, significantly outperforming traditional combinatorial search. We conducted various experiments on a variety of shapes to showcase the speed and versatility of TilinGNN. We also present comparisons to alternative methods and manual solutions, robustness analysis, and ablation studies to demonstrate the quality of our approach.
Langevin monte carlo rendering with gradient-based adaptation
Authors: Luan Fujun , Zhao Shuang , Bala Kavita , Gkioulekas Ioannis
Published: August 2020

We introduce a suite of Langevin Monte Carlo algorithms for efficient photorealistic rendering of scenes with complex light transport effects, such as caustics, interreflections, and occlusions. Our algorithms operate in primary sample space, and use the Metropolis-adjusted Langevin algorithm (MALA) to generate new samples. Drawing inspiration from state-of-the-art stochastic gradient descent procedures, we combine MALA with adaptive preconditioning and momentum schemes that re-use previously-computed first-order gradients, either in an online or in a cache-driven fashion. This combination allows MALA to adapt to the local geometry of the primary sample space, without the computational overhead associated with previous Hessian-based adaptation algorithms. We use the theory of controlled Markov chain Monte Carlo to ensure that these combinations remain ergodic, and are therefore suitable for unbiased Monte Carlo rendering. Through extensive experiments, we show that our algorithms, MALA with online and cache-driven adaptation, can successfully handle complex light transport in a large variety of scenes, leading to improved performance (on average more than 3× variance reduction at equal time, and 7× for motion blur) compared to state-of-the-art Markov chain Monte Carlo rendering algorithms.
Fast tetrahedral meshing in the wild
Authors: Hu Yixin , Schneider Teseo , Wang Bolun , Zorin Denis , Panozzo Daniele
Published: August 2020

We propose a new tetrahedral meshing method, fTetWild, to convert triangle soups into high-quality tetrahedral meshes. Our method builds on the TetWild algorithm, replacing the rational triangle insertion with a new incremental approach to construct and optimize the output mesh, interleaving triangle insertion and mesh optimization. Our approach makes it possible to maintain a valid floating-point tetrahedral mesh at all algorithmic stages, eliminating the need for costly constructions with rational numbers used by TetWild, while maintaining full robustness and similar output quality. This allows us to improve on TetWild in two ways. First, our algorithm is significantly faster, with running time comparable to less robust Delaunay-based tetrahedralization algorithms. Second, our algorithm is guaranteed to produce a valid tetrahedral mesh with floating-point vertex coordinates, while TetWild produces a valid mesh with rational coordinates which is not guaranteed to be valid after floating-point conversion. As a trade-off, our algorithm no longer guarantees that all input triangles are present in the output mesh, but in practice, as confirmed by our tests on the Thingi10k dataset, the algorithm always succeeds in inserting all input triangles.
Tactile line drawings for improved shape understanding in blind and visually impaired users
Authors: Panotopoulou Athina , Zhang Xiaoting , Qiu Tammy , Yang Xing-Dong , Whiting Emily
Published: August 2020

Members of the blind and visually impaired community rely heavily on tactile illustrations - raised line graphics on paper that are felt by hand - to understand geometric ideas in school textbooks, depict a story in children's books, or conceptualize exhibits in museums. However, these illustrations often fail to achieve their goals, in large part due to the lack of understanding in how 3D shapes can be represented in 2D projections. This paper describes a new technique to design tactile illustrations considering the needs of blind individuals. Successful illustration design of 3D objects presupposes identification and combination of important information in topology and geometry. We propose a twofold approach to improve shape understanding. First, we introduce a part-based multi-projection rendering strategy to display geometric information of 3D shapes, making use of canonical viewpoints and removing reliance on traditional perspective projections. Second, curvature information is extracted from cross sections and embedded as textures in our illustrations.
Discrete differential operators on polygonal meshes
Authors: De Goes Fernando , Butts Andrew , Desbrun Mathieu
Published: August 2020

Geometry processing of surface meshes relies heavily on the discretization of differential operators such as gradient, Laplacian, and covariant derivative. While a variety of discrete operators over triangulated meshes have been developed and used for decades, a similar construction over polygonal meshes remains far less explored despite the prevalence of non-simplicial surfaces in geometric design and engineering applications. This paper introduces a principled construction of discrete differential operators on surface meshes formed by (possibly non-flat and non-convex) polygonal faces. Our approach is based on a novel mimetic discretization of the gradient operator that is linear-precise on arbitrary polygons. Equipped with this discrete gradient, we draw upon ideas from the Virtual Element Method in order to derive a series of discrete operators commonly used in graphics that are now valid over polygonal surfaces. We demonstrate the accuracy and robustness of our resulting operators through various numerical examples, before incorporating them into existing geometry processing algorithms.
Portrait shadow manipulation
Authors: Zhang Xuaner (Cecilia) , Barron Jonathan T. , Tsai Yun-Ta , Pandey Rohit , Zhang Xiuming , Ng Ren , Jacobs David E.
Published: August 2020

Casually-taken portrait photographs often suffer from unflattering lighting and shadowing because of suboptimal conditions in the environment. Aesthetic qualities such as the position and softness of shadows and the lighting ratio between the bright and dark parts of the face are frequently determined by the constraints of the environment rather than by the photographer. Professionals address this issue by adding light shaping tools such as scrims, bounce cards, and flashes. In this paper, we present a computational approach that gives casual photographers some of this control, thereby allowing poorly-lit portraits to be relit post-capture in a realistic and easily-controllable way. Our approach relies on a pair of neural networks---one to remove foreign shadows cast by external objects, and another to soften facial shadows cast by the features of the subject and to add a synthetic fill light to improve the lighting ratio. To train our first network we construct a dataset of real-world portraits wherein synthetic foreign shadows are rendered onto the face, and we show that our network learns to remove those unwanted shadows. To train our second network we use a dataset of Light Stage scans of human subjects to construct input/output pairs of input images harshly lit by a small light source, and variably softened and fill-lit output images of each face. We propose a way to explicitly encode facial symmetry and show that our dataset and training procedure enable the model to generalize to images taken in the wild. Together, these networks enable the realistic and aesthetically pleasing enhancement of shadows and lights in real-world portrait images.
1
Converting stroked primitives to filled primitives
Authors: Nehab Diego
Published: August 2020

Vector graphics formats offer support for both filled and stroked primitives. Filled primitives paint all points in the region bounded by a set of outlines. Stroked primitives paint all points covered by a line drawn over the outlines. Editors allow users to convert stroked primitives to the outlines of equivalent filled primitives for further editing. Likewise, renderers typically convert stroked primitives to equivalent filled primitives prior to rendering. This conversion problem is deceivingly difficult to solve. Surprisingly, it has received little to no attention in the literature. Existing implementations output too many segments, do not satisfy accuracy requirements, or fail under a variety of conditions, often spectacularly. In this paper, we present a solution to the stroke-to-fill conversion problem that addresses these issues. One of our key insights is to take into account the evolutes of input outlines, in addition to their offsets, in regions of high curvature. Furthermore, our approach strives to maintain continuity between the input and the set of painted points. Our implementation is available in open source.
The design and evolution of the UberBake light baking system
Authors: Seyb Dario , Sloan Peter-Pike , Silvennoinen Ari , Iwanicki Michał , Jarosz Wojciech
Published: August 2020

We describe the design and evolution of UberBake, a global illumination system developed by Activision, which supports limited lighting changes in response to certain player interactions. Instead of relying on a fully dynamic solution, we use a traditional static light baking pipeline and extend it with a small set of features that allow us to dynamically update the precomputed lighting at run-time with minimal performance and memory overhead. This means that our system works on the complete set of target hardware, ranging from high-end PCs to previous generation gaming consoles, allowing the use of lighting changes for gameplay purposes. In particular, we show how to efficiently precompute lighting changes due to individual lights being enabled and disabled and doors opening and closing. Finally, we provide a detailed performance evaluation of our system using a set of production levels and discuss how to extend its dynamic capabilities in the future.
Sliced optimal transport sampling
Authors: Paulin Lois , Bonneel Nicolas , Coeurjolly David , Iehl Jean-Claude , Webanck Antoine , Desbrun Mathieu , Ostromoukhov Victor
Published: August 2020

In this paper, we introduce a numerical technique to generate sample distributions in arbitrary dimension for improved accuracy of Monte Carlo integration. We point out that optimal transport offers theoretical bounds on Monte Carlo integration error, and that the recently-introduced numerical framework of sliced optimal transport (SOT) allows us to formulate a novel and efficient approach to generating well-distributed high-dimensional pointsets. The resulting sliced optimal transport sampling, solely involving repeated 1D solves, is particularly simple and efficient for the common case of a uniform density over a
d
-dimensional ball. We also construct a volume-preserving map from a
d
-ball to a
d
-cube (generalizing the Shirley-Chiu mapping to arbitrary dimensions) to offer fast SOT sampling over
d
-cubes. We provide ample numerical evidence of the improvement in Monte Carlo integration accuracy that SOT sampling brings compared to existing QMC techniques, and derive a projective variant for rendering which rivals, and at times outperforms, current sampling strategies using low-discrepancy sequences or optimized samples.
Tactile rendering based on skin stress optimization
Authors: Verschoor Mickeal , Casas Dan , Otaduy Miguel A.
Published: August 2020

We present a method to render virtual touch, such that the stimulus produced by a tactile device on a user's skin matches the stimulus computed in a virtual environment simulation. To achieve this, we solve the inverse mapping from skin stimulus to device configuration thanks to a novel optimization algorithm. Within this algorithm, we use a device-skin simulation model to estimate rendered stimuli, we account for trajectory-dependent effects efficiently by decoupling the computation of the friction state from the optimization of device configuration, and we accelerate computations using a neural-network approximation of the device-skin model. Altogether, we enable real-time tactile rendering of rich interactions including smooth rolling, but also contact with edges, or frictional stick-slip motion. We validate our algorithm both qualitatively through user experiments, and quantitatively on a BioTac biomimetic finger sensor.
Inter-surface maps via constant-curvature metrics
Authors: Schmidt Patrick , Campen Marcel , Born Janis , Kobbelt Leif
Published: August 2020

We propose a novel approach to represent maps between two discrete surfaces of the same genus and to minimize intrinsic mapping distortion. Our maps are well-defined at every surface point and are guaranteed to be continuous bijections (surface homeomorphisms). As a key feature of our approach, only the images of vertices need to be represented explicitly, since the images of all other points (on edges or in faces) are properly defined implicitly. This definition is via unique geodesics in metrics of constant Gaussian curvature. Our method is built upon the fact that such metrics exist on surfaces of arbitrary topology, without the need for any cuts or cones (as asserted by the uniformization theorem). Depending on the surfaces' genus, these metrics exhibit one of the three classical geometries: Euclidean, spherical or hyperbolic. Our formulation handles constructions in all three geometries in a unified way. In addition, by considering not only the vertex images but also the discrete metric as degrees of freedom, our formulation enables us to simultaneously optimize the images of these vertices and images of all other points.
Specular manifold sampling for rendering high-frequency caustics and glints
Authors: Zeltner Tizian , Georgiev Iliyan , Jakob Wenzel
Published: August 2020

Scattering from specular surfaces produces complex optical effects that are frequently encountered in realistic scenes: intricate caustics due to focused reflection, multiple refraction, and high-frequency glints from specular microstructure. Yet, despite their importance and considerable research to this end, sampling of light paths that cause these effects remains a formidable challenge.

In this article, we propose a surprisingly simple and general sampling strategy for specular light paths including the above examples, unifying the previously disjoint areas of caustic and glint rendering into a single framework. Given two path vertices, our algorithm stochastically finds a specular subpath connecting the endpoints. In contrast to prior work, our method supports high-frequency normal- or displacement-mapped geometry, samples specular-diffuse-specular ("SDS") paths, and is compatible with standard Monte Carlo methods including unidirectional path tracing. Both unbiased and biased variants of our approach can be constructed, the latter often significantly reducing variance, which may be appealing in applied settings (e.g. visual effects). We demonstrate our method on a range of challenging scenes and evaluate it against state-of-the-art methods for rendering caustics and glints.
Human-in-the-loop differential subspace search in high-dimensional latent space
Authors: Chiu Chia-Hsing , Koyama Yuki , Lai Yu-Chi , Igarashi Takeo , Yue Yonghao
Published: August 2020

Generative models based on deep neural networks often have a high-dimensional latent space, ranging sometimes to a few hundred dimensions or even higher, which typically makes them hard for a user to explore directly. We propose
differential subspace search
to allow efficient iterative user exploration in such a space, without relying on domain- or data-specific assumptions. We develop a general framework to extract low-dimensional subspaces based on a local differential analysis of the generative model, such that a small change in such a subspace would provide enough change in the resulting data. We do so by applying singular value decomposition to the Jacobian of the generative model and forming a subspace with the desired dimensionality spanned by a given number of singular vectors stochastically selected on the basis of their singular values, to maintain ergodicity. We use our framework to present 1D subspaces to the user via a 1D slider interface. Starting from an initial location, the user finds a new candidate in the presented 1D subspace, which is in turn updated at the new candidate location. This process is repeated until no further improvement can be made. Numerical simulations show that our method can better optimize synthetic black-box objective functions than the alternatives that we tested. Furthermore, we conducted a user study using complex generative models and the results show that our method enables more efficient exploration of high-dimensional latent spaces than the alternatives.
Holographic optics for thin and lightweight virtual reality
Authors: Maimone Andrew , Wang Junren
Published: August 2020

We present a class of display designs combining holographic optics, directional backlighting, laser illumination, and polarization-based optical folding to achieve thin, lightweight, and high performance near-eye displays for virtual reality. Several design alternatives are proposed, compared, and experimentally validated as prototypes. Using only thin, flat films as optical components, we demonstrate VR displays with thicknesses of less than 9 mm, fields of view of over 90° horizontally, and form factors approaching sunglasses. In a benchtop form factor, we also demonstrate a full color display using wavelength-multiplexed holographic lenses that uses laser illumination to provide a large gamut and highly saturated color. We show experimentally that our designs support resolutions expected of modern VR headsets and can scale to human visual acuity limits. Current limitations are identified, and we discuss challenges to obtain full practicality.
Developability of heightfields via rank minimization
Authors: Sellán Silvia , Aigerman Noam , Jacobson Alec
Published: August 2020

This work concerns the computation and approximation of developable surfaces --- surfaces that are locally isometric to the two-dimensional plane. These surfaces are heavily studied in differential geometry, and are also of great interest to fabrication, architecture and fashion. We focus specifically on developability of heightfields. Our main observation is that developability can be cast as a rank constraint, which can then be plugged into theoretically-grounded rank-minimization techniques from the field of compressed sensing. This leads to a convex semidefinite optimization problem, which receives an input heightfield and recovers a similar heightfield which is developable. Due to the sparsifying nature of compressed sensing, the recovered surface is piecewise developable, with creases emerging between connected developable pieces. The convex program includes one user-specified parameter, balancing adherence to the original surface with developability and number of patches. We moreover show, that in contrast to previous techniques, our discretization does not introduce a bias and the same results are achieved across resolutions and orientations, and with no limit on the number of creases and patches. We solve this convex semidefinite optimization problem efficiently, by devising a tailor-made ADMM solver which leverages matrix-projection observations unique to our problem. We employ our method on a plethora of experiments, from denoising 3D scans of developable geometry such as documents and buildings, through approximating general heightfields with developable ones, and up to interpolating sparse annotations with a developable heightfield.
Incremental potential contact
Authors: Li Minchen , Ferguson Zachary , Schneider Teseo , Langlois Timothy , Zorin Denis , Panozzo Daniele , Jiang Chenfanfu , Kaufman Danny M.
Published: August 2020

Contacts weave through every aspect of our physical world, from daily household chores to acts of nature. Modeling and predictive computation of these phenomena for solid mechanics is important to every discipline concerned with the motion of mechanical systems, including engineering and animation. Nevertheless, efficiently time-stepping accurate and consistent simulations of real-world contacting elastica remains an outstanding computational challenge. To model the complex interaction of deforming solids in contact we propose Incremental Potential Contact (IPC) - a new model and algorithm for variationally solving implicitly time-stepped nonlinear elastodynamics. IPC maintains an intersection- and inversion-free trajectory regardless of material parameters, time step sizes, impact velocities, severity of deformation, or boundary conditions enforced.

Constructed with a custom nonlinear solver, IPC enables efficient resolution of time-stepping problems with separate, user-exposed accuracy tolerances that allow independent specification of the physical accuracy of the dynamics and the geometric accuracy of surface-to-surface conformation. This enables users to decouple, as needed per application, desired accuracies for a simulation's dynamics and geometry.

The resulting time stepper solves contact problems that are intersection-free (and thus robust), inversion-free, efficient (at speeds comparable to or faster than available methods that lack both convergence and feasibility), and accurate (solved to user-specified accuracies). To our knowledge this is the first implicit time-stepping method, across both the engineering and graphics literature that can consistently enforce these guarantees as we vary simulation parameters.

In an extensive comparison of available simulation methods, research libraries and commercial codes we confirm that available engineering and computer graphics methods, while each succeeding admirably in custom-tuned regimes, often fail with instabilities, egregious constraint violations and/or inaccurate and implausible solutions, as we vary input materials, contact numbers and time step. We also exercise IPC across a wide range of existing and new benchmark tests and demonstrate its accurate solution over a broad sweep of reasonable time-step sizes and beyond (up to
h
= 2s) across challenging large-deformation, large-contact stress-test scenarios with meshes composed of up to 2.3M tetrahedra and processing up to 498K contacts per time step. For applications requiring high-accuracy we demonstrate tight convergence on all measures. While, for applications requiring lower accuracies, e.g. animation, we confirm IPC can ensure feasibility and plausibility even when specified tolerances are lowered for efficiency.
AnisoMPM
Authors: Wolper Joshuah , Chen Yunuo , Li Minchen , Fang Yu , Qu Ziyin , Lu Jiecong , Cheng Meggie , Jiang Chenfanfu
Published: August 2020

Dynamic fracture surrounds us in our day-to-day lives, but animating this phenomenon is notoriously difficult and only further complicated by anisotropic materials---those with underlying structures that dictate preferred fracture directions. Thus, we present AnisoMPM: a robust and general approach for animating the dynamic fracture of isotropic, transversely isotropic, and orthotropic materials. AnisoMPM has three core components: a technique for anisotropic damage evolution, methods for anisotropic elastic response, and a coupling approach. For anisotropic damage, we adopt a non-local continuum damage mechanics (CDM) geometric approach to crack modeling and augment this with structural tensors to encode material anisotropy. Furthermore, we discretize our damage evolution with explicit and implicit integration, giving a high degree of computational efficiency and flexibility. We also utilize a QR-decomposition based anisotropic constitutive model that is inversion safe, more efficient than SVD models, easy to implement, robust to extreme deformations, and that captures all aforementioned modes of anisotropy. Our elasto-damage coupling is enforced through an additive decomposition of our hyperelasticity into a tensile and compressive component in which damage is used to degrade the tensile contribution to allow for material separation. For extremely stiff fibered materials, we further introduce a novel Galerkin weak form discretization that enables embedded directional inextensibility. We present this as a hard-constrained grid velocity solve that poses an alternative to our anisotropic elasticity that is locking-free and can model very stiff materials.
A level-set method for magnetic substance simulation
Authors: Ni Xingyu , Zhu Bo , Wang Bin , Chen Baoquan
Published: August 2020

We present a versatile numerical approach to simulating various magnetic phenomena using a level-set method. At the heart of our method lies a novel two-way coupling mechanism between a magnetic field and a magnetizable mechanical system, which is based on the interfacial Helmholtz force drawn from the Minkowski form of the Maxwell stress tensor. We show that a magnetic-mechanical coupling system can be solved as an interfacial problem, both theoretically and computationally. In particular, we employ a Poisson equation with a jump condition across the interface to model the mechanical-to-magnetic interaction and a Helmholtz force on the free surface to model the magnetic-to-mechanical effects. Our computational framework can be easily integrated into a standard Euler fluid solver, enabling both simulation and visualization of a complex magnetic field and its interaction with immersed magnetizable objects in a large domain. We demonstrate the efficacy of our method through an array of magnetic substance simulations that exhibit rich geometric and dynamic characteristics, encompassing ferrofluid, rigid magnetic body, deformable magnetic body, and multi-phase couplings.
Automatic structure synthesis for 3D woven relief
Authors: Wu Rundong , Harvey Claire , Zhang Joy Xiaoji , Kroszner Sean , Hagan Brooks , Marschner Steve
Published: August 2020

3D weaving is a manufacturing technique that creates multilayer textiles with substantial thickness. Currently, the primary use for these materials is in regularly structured carbon-polymer or glass-polymer composites, but in principle a wide range of complex shapes can be achieved, providing the opportunity to customize the fiber structure for individual parts and also making 3D weaving appealing in many soft-goods applications. The primary obstacle to broader use is the need to design intricate weave structures, involving tens to hundreds of thousands of yarn crossings, which are different for every shape to be produced. The goal of this research is to make 3D weaving as readily usable as CNC machining or 3D printing, by providing an algorithm to convert an arbitrary 3D solid model into machine instructions to weave the corresponding shape. We propose a method to generate 3D weaving patterns for height fields by slicing the shape along intersecting arrays of parallel planes and then computing the paths for all the warp and weft yarns, which travel in these planes. We demonstrate the method by generating weave structures for different shapes and fabricating a number of examples in polyester yarn using a Jacquard loom.
Interactive video stylization using few-shot patch-based training
Authors: Texler Ondřej , Futschik David , kučera Michal , jamriška Ondřej , Sochorová Šárka , Chai Menclei , Tulyakov Sergey , SÝkora Daniel
Published: August 2020

In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence. Its key advantage is that the resulting stylization is semantically meaningful, i.e., specific parts of moving objects are stylized according to the artist's intention. In contrast to previous style transfer techniques, our approach does not require any lengthy pre-training process nor a large training dataset. We demonstrate how to train an appearance translation network from scratch using only a few stylized exemplars while implicitly preserving temporal consistency. This leads to a video stylization framework that supports real-time inference, parallel processing, and random access to an arbitrary output frame. It can also merge the content from multiple keyframes without the need to perform an explicit blending operation. We demonstrate its practical utility in various interactive scenarios, where the user paints over a selected keyframe and sees her style transferred to an existing recorded sequence or a live video stream.
Attribute2Font
Authors: Wang Yizhi , Gao Yue , Lian Zhouhui
Published: August 2020

Font design is now still considered as an exclusive privilege of professional designers, whose creativity is not possessed by existing software systems. Nevertheless, we also notice that most commercial font products are in fact manually designed by following specific requirements on some attributes of glyphs, such as italic, serif, cursive, width, angularity, etc. Inspired by this fact, we propose a novel model, Attribute2Font, to automatically create fonts by synthesizing visually pleasing glyph images according to user-specified attributes and their corresponding values. To the best of our knowledge, our model is the first one in the literature which is capable of generating glyph images in new font styles, instead of retrieving existing fonts, according to given values of specified font attributes. Specifically, Attribute2Font is trained to perform font style transfer between any two fonts conditioned on their attribute values. After training, our model can generate glyph images in accordance with an arbitrary set of font attribute values. Furthermore, a novel unit named Attribute Attention Module is designed to make those generated glyph images better embody the prominent font attributes. Considering that the annotations of font attribute values are extremely expensive to obtain, a semi-supervised learning scheme is also introduced to exploit a large number of unlabeled fonts. Experimental results demonstrate that our model achieves impressive performance on many tasks, such as creating glyph images in new font styles, editing existing fonts, interpolation among different fonts, etc.
Directional sources and listeners in interactive sound propagation using reciprocal wave field coding
Authors: Chaitanya Chakravarty R. Alla , Raghuvanshi Nikunj , Godin Keith W. , Zhang Zechen , Nowrouzezahrai Derek , Snyder John M.
Published: August 2020

Common acoustic sources, like voices or musical instruments, exhibit strong frequency and directional dependence. When transported through complex environments, their anisotropic radiated field undergoes scattering, diffraction, and occlusion before reaching a directionally-sensitive listener. We present the first wave-based interactive auralization system that encodes and renders a complete reciprocal description of acoustic wave fields in general scenes. Our method renders directional effects at freely moving and rotating sources and listeners and supports any tabulated
source directivity function
and head-related transfer function. We represent a static scene's global acoustic transfer as an 11-dimensional
bidirectional impulse response
(BIR) field, which we extract from a set of wave simulations. We parametrically encode the BIR as a pair of radiating and arriving directions for the perceptually-salient initial (
direct
) response, and a compact 6 × 6
reflections transfer matrix
capturing indirect energy transfer with scene-dependent anisotropy. We render our encoded data with an efficient and scalable algorithm - integrated in the Unreal Engine
™
- whose CPU performance is agnostic to scene complexity and angular source/listener resolutions. We demonstrate convincing effects that depend on detailed scene geometry, for a variety of environments and source types.
A practical octree liquid simulator with adaptive surface resolution
Authors: Ando Ryoichi , Batty Christopher
Published: August 2020

We propose a new adaptive liquid simulation framework that achieves highly detailed behavior with reduced implementation complexity. Prior work has shown that spatially adaptive grids are efficient for simulating large-scale liquid scenarios, but in order to enable adaptivity
along the liquid surface
these methods require either expensive boundary-conforming (re-)meshing or elaborate treatments for second order accurate interface conditions. This complexity greatly increases the difficulty of implementation and maintainability, potentially making it infeasible for practitioners. We therefore present new algorithms for adaptive simulation that are comparatively easy to implement yet efficiently yield high quality results. First, we develop a novel staggered octree Poisson discretization for free surfaces that is second order in pressure and gives smooth surface motions even across octree T-junctions, without a power/Voronoi diagram construction. We augment this discretization with an adaptivity-compatible surface tension force that likewise supports T-junctions. Second, we propose a moving least squares strategy for level set and velocity interpolation that requires minimal knowledge of the local tree structure while blending near-seamlessly with standard trilinear interpolation in uniform regions. Finally, to maximally exploit the flexibility of our new surface-adaptive solver, we propose several novel extensions to sizing function design that enhance its effectiveness and flexibility. We perform a range of rigorous numerical experiments to evaluate the reliability and limitations of our method, as well as demonstrating it on several complex high-resolution liquid animation scenarios.
Skeleton-aware networks for deep motion retargeting
Authors: Aberman Kfir , Li Peizhuo , Lischinski Dani , Sorkine-Hornung Olga , Cohen-Or Daniel , Chen Baoquan
Published: August 2020

We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Importantly, our approach learns how to retarget without requiring any explicit pairing between the motions in the training set.

We leverage the fact that different homeomorphic skeletons may be reduced to a common
primal skeleton
by a sequence of edge merging operations, which we refer to as
skeletal pooling.
Thus, our main technical contribution is the introduction of novel differentiable convolution, pooling, and unpooling operators. These operators are
skeleton-aware
, meaning that they explicitly account for the skeleton's hierarchical structure and joint adjacency, and together they serve to transform the original motion into a collection of deep temporal features associated with the joints of the primal skeleton. In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons. Thus, retargeting can be achieved simply by encoding to, and decoding from this latent space.

Our experiments show the effectiveness of our framework for motion retargeting, as well as motion processing in general, compared to existing approaches. Our approach is also quantitatively evaluated on a synthetic dataset that contains pairs of motions applied to different skeletons. To the best of our knowledge, our method is the first to perform retargeting between skeletons with differently sampled kinematic chains, without any paired examples.
Single-shot high-quality facial geometry and skin appearance capture
Authors: Riviere Jérémy , Gotardo Paulo , Bradley Derek , Ghosh Abhijeet , Beeler Thabo
Published: August 2020

We propose a new light-weight face capture system capable of reconstructing both
high-quality geometry and detailed appearance maps
from a
single exposure.
Unlike currently employed appearance acquisition systems, the proposed technology does not require active illumination and hence can readily be integrated with passive photogrammetry solutions. These solutions are in widespread use for 3D scanning humans as they can be assembled from off-the-shelf hardware components, but lack the capability of estimating appearance. This paper proposes a solution to overcome this limitation, by adding appearance capture to photogrammetry systems. The only additional hardware requirement to these solutions is that a subset of the cameras are cross-polarized with respect to the illumination, and the remaining cameras are parallel-polarized. The proposed algorithm leverages the images with the two different polarization states to reconstruct the geometry and to recover appearance properties. We do so by means of an inverse rendering framework, which solves
per texel diffuse albedo, specular intensity, and high-resolution normals, as well as global specular roughness
considering the subsurface scattering nature of skin. We show results for a variety of human subjects of different ages and skin typology, illustrating how the captured fine-detail skin surface and subsurface scattering effects lead to realistic renderings of their digital doubles, also in different illumination conditions.
Wave curves
Authors: Skrivan Tomas , Soderstrom Andreas , Johansson John , Sprenger Christoph , Museth Ken , Wojtan Chris
Published: August 2020

We propose a method to enhance the visual detail of a water surface simulation. Our method works as a post-processing step which takes a simulation as input and increases its apparent resolution by simulating many detailed Lagrangian water waves on top of it. We extend linear water wave theory to work in non-planar domains which deform over time, and we discretize the theory using Lagrangian wave packets attached to spline curves. The method is numerically stable and trivially parallelizable, and it produces high frequency ripples with dispersive wave-like behaviors customized to the underlying fluid simulation.
C-Space tunnel discovery for puzzle path planning
Authors: Zhang Xinya , Belfer Robert , Kry Paul G. , Vouga Etienne
Published: August 2020

Rigid body disentanglement puzzles are challenging for both humans and motion planning algorithms because their solutions involve tricky twisting and sliding moves that correspond to navigating through narrow tunnels in the puzzle's configuration space (C-space). We propose a tunnel-discovery and planning strategy for solving these puzzles. First, we locate important features on the pieces using geometric heuristics and machine learning, and then match pairs of these features to discover collision free states in the puzzle's C-space that lie within the narrow tunnels. Second, we propose a Rapidly-exploring Dense Tree (RDT) motion planner variant that builds tunnel escape roadmaps and then connects these roadmaps into a solution path connecting start and goal states. We evaluate our approach on a variety of challenging disentanglement puzzles and provide extensive baseline comparisons with other motion planning techniques.
Spatiotemporal reservoir resampling for real-time ray tracing with dynamic direct lighting
Authors: Bitterli Benedikt , Wyman Chris , Pharr Matt , Shirley Peter , Lefohn Aaron , Jarosz Wojciech
Published: August 2020

Efficiently rendering direct lighting from millions of dynamic light sources using Monte Carlo integration remains a challenging problem, even for off-line rendering systems. We introduce a new algorithm---ReSTIR---that renders such lighting interactively, at high quality, and without needing to maintain complex data structures. We repeatedly resample a set of candidate light samples and apply further spatial and temporal resampling to leverage information from relevant nearby samples. We derive an unbiased Monte Carlo estimator for this approach, and show that it achieves equal-error 6×-60× faster than state-of-the-art methods. A biased estimator reduces noise further and is 35×-65× faster, at the cost of some energy loss. We implemented our approach on the GPU, rendering complex scenes containing up to 3.4 million dynamic, emissive triangles in under 50 ms per frame while tracing at most 8 rays per pixel.
Immersive light field video with a layered mesh representation
Authors: Broxton Michael , Flynn John , Overbeck Ryan , Erickson Daniel , Hedman Peter , Duvall Matthew , Dourgarian Jason , Busch Jay , Whalen Matt , Debevec Paul
Published: August 2020

We present a system for capturing, reconstructing, compressing, and rendering high quality immersive light field video. We accomplish this by leveraging the recently introduced DeepView view interpolation algorithm, replacing its underlying multi-plane image (MPI) scene representation with a collection of spherical shells that are better suited for representing panoramic light field content. We further process this data to reduce the large number of shell layers to a small, fixed number of RGBA+depth layers without significant loss in visual quality. The resulting RGB, alpha, and depth channels in these layers are then compressed using conventional texture atlasing and video compression techniques. The final compressed representation is lightweight and can be rendered on mobile VR/AR platforms or in a web browser. We demonstrate light field video results using data from the 16-camera rig of [Pozo et al. 2019] as well as a new low-cost hemispherical array made from 46 synchronized action sports cameras. From this data we produce 6 degree of freedom volumetric videos with a wide 70 cm viewing baseline, 10 pixels per degree angular resolution, and a wide field of view, at 30 frames per second video frame rates. Advancing over previous work, we show that our system is able to reproduce challenging content such as view-dependent reflections, semi-transparent surfaces, and near-field objects as close as 34 cm to the surface of the camera rig.