bluuebunny/crossref_metadata_2025 huggingface dataset:
RedMax
Authors: Wang Ying , Weidner Nicholas J. , Baxter Margaret A. , Hwang Yura , Kaufman Danny M. , Sueda Shinjiro
Published: July 2019

It is well known that the dynamics of articulated rigid bodies can be solved in
O
(n)
time using a recursive method, where
n
is the number of joints. However, when elasticity is added between the bodies
(e.g.
, damped springs), with linearly implicit integration, the stiffness matrix in the equations of motion breaks the tree topology of the system, making the recursive
O
(n)
method inapplicable. In such cases, the only alternative has been to form and solve the system matrix, which takes
O
(
n
3
) time. We propose a new approach that is capable of solving the linearly implicit equations of motion in near linear time. Our method, which we call RedMax, is built using a combined reduced/maximal coordinate formulation. This hybrid model enables direct flexibility to apply arbitrary combinations of constraints and contact modeling in both reduced and maximal coordinates, as well as mixtures of implicit and explicit forces in either coordinate representation. We highlight RedMax's flexibility with seamless integration of deformable objects with two-way coupling, at a standard additional cost. We further highlight its flexibility by constructing an efficient internal (joint) and external (environment) frictional contact solver that can leverage bilateral joint constraints for rapid evaluation of frictional articulated dynamics.
Spectral coarsening of geometric operators
Authors: Liu Hsueh-Ti Derek , Jacobson Alec , Ovsjanikov Maks
Published: July 2019

We introduce a novel approach to measure the behavior of a geometric operator before and after coarsening. By comparing eigenvectors of the input operator and its coarsened counterpart, we can quantitatively and visually analyze how well the spectral properties of the operator are maintained. Using this measure, we show that standard mesh simplification and algebraic coarsening techniques fail to maintain spectral properties. In response, we introduce a novel approach forspectral coarsening.We show that it is possible to significantly reduce the sampling density of an operator derived from a 3D shape without affecting the low-frequency eigenvectors. By marrying techniques developed within the algebraic multigrid and the functional maps literatures, we successfully coarsen a variety of isotropic and anisotropic operators while maintaining sparsity and positive semi-definiteness. We demonstrate the utility of this approach for applications including operatorsensitive sampling, shape matching, and graph pooling for convolutional neural networks.
Sample-based Monte Carlo denoising using a kernel-splatting network
Authors: Gharbi Michaël , Li Tzu-Mao , Aittala Miika , Lehtinen Jaakko , Durand Frédo
Published: July 2019

Denoising has proven to be useful to efficiently generate high-quality Monte Carlo renderings. Traditional pixel-based denoisers exploit summary statistics of a pixel's sample distributions, which discards much of the samples' information and limits their denoising power. On the other hand, sample-based techniques tend to be slow and have difficulties handling general transport scenarios. We present the first convolutional network that can learn to denoise Monte Carlo renderings directly from the samples. Learning the mapping between samples and images creates new challenges for the network architecture design: the order of the samples is arbitrary, and they should be treated in a permutation invariant manner. To address these challenges, we develop a novel kernel-predicting architecture that
splats
individual samples onto nearby pixels. Splatting is a natural solution to situations such as motion blur, depth-of-field and many light transport paths, where it is easier to predict which pixels a sample contributes to, rather than a
gather
approach that needs to figure out, for each pixel, which samples (or nearby pixels) are relevant. Compared to previous state-of-the-art methods, ours is robust to the severe noise of low-sample count images (e.g. 8 samples per pixel) and yields higher-quality results both visually and numerically. Our approach retains the generality and efficiency of pixel-space methods while enjoying the expressiveness and accuracy of the more complex sample-based approaches.
Rodent
Authors: Pérard-Gayot Arsène , Membarth Richard , Leißa Roland , Hack Sebastian , Slusallek Philipp
Published: July 2019

Monte-Carlo Renderers must generate many color samples to produce a noise-free image, and for each of those, they must evaluate complex mathematical models representing the appearance of the objects in the scene. These models are usually in the form of shaders: Small programs that are executed during rendering in order to compute a value for the current sample.

Renderers often compile and optimize shaders just before rendering, taking advantage of the knowledge of the scene. In principle, the entire renderer could benefit from a-priori code generation. For instance, scheduling can take advantage of the knowledge of the scene in order to maximize hardware usage. However, writing such a configurable renderer eventually means writing a compiler that translates a scene description into machine code.

In this paper, we present a framework that allows generating entire renderers for CPUs and GPUs without having to write a dedicated compiler: First, we provide a rendering library in a functional/imperative language that elegantly abstracts the individual rendering concepts using higher-order functions. Second, we use
partial evaluation
to combine and specialize the individual components of a renderer according to a particular scene.

Our results show that the renderers we generate outperform equivalent high-performance implementations written with state-of-the-art ray tracing libraries on the CPU and GPU.
SAGNet
Authors: Wu Zhijie , Wang Xiang , Lin Di , Lischinski Dani , Cohen-Or Daniel , Huang Hui
Published: July 2019

We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships between them (the structure) are jointly learned and embedded in a latent space by an autoencoder. The encoder intertwines the geometry and structure features into a single latent code, while the decoder disentangles the features and reconstructs the geometry and structure of the 3D model. Our autoencoder consists of two branches, one for the structure and one for the geometry. The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code. This explicit intertwining of information enables separately controlling the geometry and the structure of the generated models. We evaluate the performance of our method and conduct an ablation study. We explicitly show that encoding of shapes accounts for both similarities in structure and geometry. A variety of quality results generated by SAGNet are presented.
Real-time pose and shape reconstruction of two interacting hands with a single depth camera
Authors: Mueller Franziska , Davis Micah , Bernard Florian , Sotnychenko Oleksandr , Verschoor Mickeal , Otaduy Miguel A. , Casas Dan , Theobalt Christian
Published: July 2019

We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands. Our approach is the first two-hand tracking solution that combines an extensive list of favorable properties, namely it is marker-less, uses a single consumer-level depth camera, runs in real time, handles inter- and intra-hand collisions, and automatically adjusts to the user's hand shape. In order to achieve this, we embed a recent parametric hand pose and shape model and a dense correspondence predictor based on a deep neural network into a suitable energy minimization framework. For training the correspondence prediction network, we synthesize a two-hand dataset based on physical simulations that includes both hand pose and shape annotations while at the same time avoiding inter-hand penetrations. To achieve real-time rates, we phrase the model fitting in terms of a nonlinear least-squares problem so that the energy can be optimized based on a highly efficient GPU-based Gauss-Newton optimizer. We show state-of-the-art results in scenes that exceed the complexity level demonstrated by previous work, including tight two-hand grasps, significant inter-hand occlusions, and gesture interaction.
1
MeshCNN
Authors: Hanocka Rana , Hertz Amir , Fish Noa , Giryes Raja , Fleishman Shachar , Cohen-Or Daniel
Published: July 2019

Polygonal meshes provide an efficient representation for 3D shapes. They explicitly captureboth shape surface and topology, and leverage non-uniformity to represent large flat regions as well as sharp, intricate features. This non-uniformity and irregularity, however, inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations. In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using
MeshCNN
, a convolutional neural network designed specifically for triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized convolution and pooling layers that operate on the mesh edges, by leveraging their intrinsic geodesic connections. Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions. MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones. We demonstrate the effectiveness of MeshCNN on various learning tasks applied to 3D meshes.
A learned shape-adaptive subsurface scattering model
Authors: Vicini Delio , Koltun Vladlen , Jakob Wenzel
Published: July 2019

Subsurface scattering, in which light refracts into a translucent material to interact with its interior, is the dominant mode of light transport in many types of organic materials. Accounting for this phenomenon is thus crucial for visual realism, but explicit simulation of the complex internal scattering process is often too costly. BSSRDF models based on analytic transport solutions are significantly more efficient but impose severe assumptions that are almost always violated, e.g. planar geometry, isotropy, low absorption, and spatio-directional separability. The resulting discrepancies between model and usage lead to objectionable errors in renderings, particularly near geometric features that violate planarity.

This article introduces a new shape-adaptive BSSRDF model that retains the efficiency of prior analytic methods while greatly improving overall accuracy. Our approach is based on a conditional variational autoencoder, which learns to sample from a reference distribution produced by a brute-force volumetric path tracer. In contrast to the path tracer, our autoencoder directly samples outgoing locations on the object surface, bypassing a potentially lengthy internal scattering process.

The distribution is conditional on both material properties and a set of features characterizing geometric variation in a neighborhood of the incident location. We use a low-order polynomial to model the local geometry as an implicitly defined surface, capturing curvature, thickness, corners, as well as cylindrical and toroidal regions. We present several examples of objects with challenging medium parameters and complex geometry and compare to ground truth simulations and prior work.
KleinPAT
Authors: Wang Jui-Hsien , James Doug L.
Published: July 2019

We propose a new modal sound synthesis method that rapidly estimates all acoustic transfer fields of a linear modal vibration model, and greatly reduces preprocessing costs. Instead of performing a separate frequency-domain Helmholtz radiation analysis for each mode, our method partitions vibration modes into
chords
using optimal
mode conflation
, then performs a single time-domain wave simulation for each chord. We then perform
transfer deconflation
on each chord's time-domain radiation field using a specialized QR solver, and thereby extract the frequency-domain transfer functions of each mode. The precomputed transfer functions are represented for fast far-field evaluation, e.g., using multipole expansions. In this paper, we propose to use a single scalar-valued Far-field Acoustic Transfer (FFAT) cube map. We describe a GPU-accelerated vector wavesolver that achieves high-throughput acoustic transfer computation at accuracy sufficient for sound synthesis. Our implementation, KleinPAT, can achieve hundred- to thousand-fold speedups compared to existing Helmholtz-based transfer solvers, thereby enabling large-scale generation of modal sound models for audio-visual applications.
Navigating intrinsic triangulations
Authors: Sharp Nicholas , Soliman Yousuf , Crane Keenan
Published: July 2019

We present a data structure that makes it easy to run a large class of algorithms from computational geometry and scientific computing on extremely poor-quality surface meshes. Rather than changing the geometry, as in traditional remeshing, we consider
intrinsic triangulations
which connect vertices by straight paths along the exact geometry of the input mesh. Our key insight is that such a triangulation can be encoded implicitly by storing the direction and distance to neighboring vertices. The resulting
signpost data structure
then allows geometric and topological queries to be made on-demand by tracing paths across the surface. Existing algorithms can be easily translated into the intrinsic setting, since this data structure supports the same basic operations as an ordinary triangle mesh (vertex insertions, edge splits,
etc.
). The output of intrinsic algorithms can then be stored on an ordinary mesh for subsequent use; unlike previous data structures, we use a constant amount of memory and do not need to explicitly construct an
overlay mesh
unless it is specifically requested. Working in the intrinsic setting incurs little computational overhead, yet we can run algorithms on extremely degenerate inputs, including all manifold meshes from the
Thingi10k
data set. To evaluate our data structure we implement several fundamental geometric algorithms including intrinsic versions of Delaunay refinement and optimal Delaunay triangulation, approximation of Steiner trees, adaptive mesh refinement for PDEs, and computation of Poisson equations, geodesic distance, and flip-free tangent vector fields.
Luminance-contrast-aware foveated rendering
Authors: Tursun Okan Tarhan , Arabadzhiyska-Koleva Elena , Wernikowski Marek , Mantiuk Radosław , Seidel Hans-Peter , Myszkowski Karol , Didyk Piotr
Published: July 2019

Current rendering techniques struggle to fulfill quality and power efficiency requirements imposed by new display devices such as virtual reality headsets. A promising solution to overcome these problems is foveated rendering, which exploits gaze information to reduce rendering quality for the peripheral vision where the requirements of the human visual system are significantly lower. Most of the current solutions model the sensitivity as a function of eccentricity, neglecting the fact that it also is strongly influenced by the displayed content. In this work, we propose a new luminance-contrast-aware foveated rendering technique which demonstrates that the computational savings of foveated rendering can be significantly improved if local luminance contrast of the image is analyzed. To this end, we first study the resolution requirements at different eccentricities as a function of luminance patterns. We later use this information to derive a low-cost predictor of the foveated rendering parameters. Its main feature is the ability to predict the parameters using only a low-resolution version of the current frame, even though the prediction holds for high-resolution rendering. This property is essential for the estimation of required quality before the full-resolution image is rendered. We demonstrate that our predictor can efficiently drive the foveated rendering technique and analyze its benefits in a series of user experiments.
Harmonic triangulations
Authors: Alexa Marc
Published: July 2019

We introduce the notion of harmonic triangulations: a harmonic triangulation simultaneously minimizes the Dirichlet energy of all piecewise linear functions. By a famous result of Rippa, Delaunay triangulations are the harmonic triangulations of planar point sets. We prove by explicit counterexample that in 3D a harmonic triangulation does not exist in general. However, we show that bistellar flips are harmonic: if they decrease Dirichlet energy for one set of function values, they do so for all. This observation gives rise to the notion of locally harmonic triangulations. We demonstrate that locally harmonic triangulations can be efficiently computed, and efficiently reduce sliver tetrahedra. The notion of harmonic triangulation also gives rise to a scalar measure of the quality of a triangulation, which can be used to prioritize flips and optimize the position of vertices. Tetrahedral meshes generated by optimizing this function generally show better quality than Delaunay-based optimization techniques.
Foveated AR
Authors: Kim Jonghyun , Jeong Youngmo , Stengel Michael , Akşit Kaan , Albert Rachel , Boudaoud Ben , Greer Trey , Kim Joohwan , Lopes Ward , Majercik Zander , Shirley Peter , Spjut Josef , McGuire Morgan , Luebke David
Published: July 2019

We present a near-eye augmented reality display with resolution and focal depth dynamically driven by gaze tracking. The display combines a traveling microdisplay relayed off a concave half-mirror magnifier for the high-resolution foveal region, with a wide field-of-view peripheral display using a projector-based Maxwellian-view display whose nodal point is translated to follow the viewer's pupil during eye movements using a traveling holographic optical element. The same optics relay an image of the eye to an infrared camera used for gaze tracking, which in turn drives the foveal display location and peripheral nodal point. Our display supports accommodation cues by varying the focal depth of the microdisplay in the foveal region, and by rendering simulated defocus on the "always in focus" scanning laser projector used for peripheral display. The resulting family of displays significantly improves on the field-of-view, resolution, and form-factor tradeoff present in previous augmented reality designs. We show prototypes supporting 30, 40 and 60 cpd foveal resolution at a net 85° × 78° field of view per eye.
Procedural phasor noise
Authors: Tricard Thibault , Efremov Semyon , Zanni Cédric , Neyret Fabrice , Martínez Jonàs , Lefebvre Sylvain
Published: July 2019

Procedural pattern synthesis is a fundamental tool of Computer Graphics, ubiquitous in games and special effects. By calling a single procedure in every pixel - or voxel - large quantities of details are generated at low cost, enhancing textures, producing complex structures within and along surfaces. Such procedures are typically implemented as pixel shaders.

We propose a novel procedural pattern synthesis technique that exhibits desirable properties for modeling highly contrasted patterns, that are especially well suited to produce surface and microstructure details. In particular, our synthesizer affords for a precise control over the profile, orientation and distribution of the produced stochastic patterns, while allowing to grade all these parameters spatially.

Our technique defines a stochastic smooth phase field -
a phasor noise
- that is then fed into a periodic function (e.g. a sine wave), producing an oscillating field with prescribed main frequencies and preserved contrast oscillations. In addition, the profile of each oscillation is directly controllable (e.g. sine wave, sawtooth, rectangular or any 1D profile). Our technique builds upon a reformulation of Gabor noise in terms of a
phasor field
that affords for a clear separation between local intensity and phase.

Applications range from texturing to modeling surface displacements, as well as multi-material microstructures in the context of additive manufacturing.
Wallpaper pattern alignment along garment seams
Authors: Wolff Katja , Sorkine-Hornung Olga
Published: July 2019

Despite recent developments towards on-demand, individualized garment design and fabrication, the majority of processes in the fashion industry are still inefficient and heavily dependent on manual work. A significant amount of recent research in this area has been focused on supporting designers to digitally create sewing patterns and shapes, but there is little work on textured fabrics. Aligning textile patterns like stripes or plaid along garment seams requires an experienced tailor and is thus reserved only for expensive, high-end garments. We present an interactive algorithm for automatically aligning repetitive textile patterns along seams for a given garment, allowing a user to make design choices at each step of our pipeline. Our approach is based on the 17 wallpaper groups and the symmetries they exhibit. We exploit these symmetries to optimize the alignment of the sewing pattern with the textured fabric for each of its pieces, determining where to cut the fabric. We optionally alter the sewing pattern slightly for a perfect fit along seams, without visibly changing the 3D shape of the garment. The pieces can then be cut automatically by a CNC or laser cutter. Our approach fits within the pipeline of digital garment design, eliminating the difficult, manual step of aligning and cutting the garment pieces by hand.
Visual knitting machine programming
Authors: Narayanan Vidya , Wu Kui , Yuksel Cem , McCann James
Published: July 2019

Industrial knitting machines are commonly used to manufacture complicated shapes from yarns; however, designing patterns for these machines requires extensive training. We present the first general visual programming interface for creating 3D objects with complex surface finishes on industrial knitting machines. At the core of our interface is a new, augmented, version of the stitch mesh data structure. The augmented stitch mesh stores low-level knitting operations per-face and encodes the dependencies between faces using directed edge labels. Our system can generate knittable augmented stitch meshes from 3D models, allows users to edit these meshes in a way that preserves their knittability, and can schedule the execution order and location of each face for production on a knitting machine. Our system is general, in that its knittability-preserving editing operations are sufficient to transform between any two machine-knittable stitch patterns with the same orientation on the same surface. We demonstrate the power and flexibility of our pipeline by using it to create and knit objects featuring a wide range of patterns and textures, including intarsia and Fair Isle colorwork; knit and purl textures; cable patterns; and laces.
Hyperparameter optimization in black-box image processing using differentiable proxies
Authors: Tseng Ethan , Yu Felix , Yang Yuting , Mannan Fahim , Arnaud Karl ST. , Nowrouzezahrai Derek , Lalonde Jean-François , Heide Felix
Published: July 2019

Nearly every commodity imaging system we directly interact with, or indirectly rely on, leverages power efficient, application-adjustable black-box hardware image signal processing (ISPs) units, running either in dedicated hardware blocks, or as proprietary software modules on programmable hardware. The configuration parameters of these black-box ISPs often have complex interactions with the output image, and must be adjusted prior to deployment according to application-specific quality and performance metrics. Today, this search is commonly performed
manually
by "golden eye" experts or algorithm developers leveraging domain expertise. We present a
fully automatic
system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i.e., application-specific) metric. We leverage a
differentiable
mapping between the configuration space and evaluation metrics, parameterized by a convolutional neural network that we train in an end-to-end fashion with imaging hardware in-the-loop. Unlike prior art, our
differentiable proxies
allow for high-dimension parameter search with stochastic first-order optimizers, without explicitly modeling any lower-level image processing transformations. As such, we can efficiently optimize black-box image processing pipelines for a variety of imaging applications, reducing application-specific configuration times from months to hours. Our optimization method is fully automatic, even with black-box hardware in the loop. We validate our method on experimental data for real-time display applications, object detection, and extreme low-light imaging. The proposed approach outperforms manual search qualitatively and quantitatively for all domain-specific applications tested. When applied to traditional denoisers, we demonstrate that---just by changing hyperparameters---traditional algorithms can outperform recent deep learning methods by a substantial margin on recent benchmarks.
Atlas refinement with bounded packing efficiency
Authors: Liu Hao-Yu , Fu Xiao-Ming , Ye Chunyang , Chai Shuangming , Liu Ligang
Published: July 2019

We present a novel algorithm to refine an input atlas with bounded packing efficiency. Central to this method is the use of the axis-aligned structure that converts the general polygon packing problem to a rectangle packing problem, which is easier to achieve high packing efficiency. Given a parameterized mesh with no flipped triangles, we propose a new angle-driven deformation strategy to transform it into a set of axis-aligned charts, which can be decomposed into rectangles by the motorcycle graph algorithm. Since motorcycle graphs are not unique, we select the one balancing the trade-off between the packing efficiency and chart boundary length, while maintaining bounded packing efficiency. The axis-aligned chart often contains greater distortion than the input, so we try to reduce the distortion while bounding the packing efficiency and retaining bijection. We demonstrate the efficacy of our method on a data set containing over five thousand complex models. For all models, our method is able to produce packed atlases with bounded packing efficiency; for example, when the packing efficiency bound is set to 80%, we elongate the boundary length by an average of 78.7% and increase the distortion by an average of 0.0533%. Compared to state-of-the-art methods, our method is much faster and achieves greater packing efficiency.
Single image portrait relighting
Authors: Sun Tiancheng , Barron Jonathan T. , Tsai Yun-Ta , Xu Zexiang , Yu Xueming , Fyffe Graham , Rhemann Christoph , Busch Jay , Debevec Paul , Ramamoorthi Ravi
Published: July 2019

Lighting plays a central role in conveying the essence and depth of the subject in a portrait photograph. Professional photographers will carefully control the lighting in their studio to manipulate the appearance of their subject, while consumer photographers are usually constrained to the illumination of their environment. Though prior works have explored techniques for relighting an image, their utility is usually limited due to requirements of specialized hardware, multiple images of the subject under controlled or known illuminations, or accurate models of geometry and reflectance. To this end, we present a system for
portrait relighting
: a neural network that takes as input a
single
RGB image of a portrait taken with a standard cellphone camera in an unconstrained environment, and from that image produces a relit image of that subject as though it were illuminated according to any provided environment map. Our method is trained on a small database of 18 individuals captured under different directional light sources in a controlled light stage setup consisting of a densely sampled sphere of lights. Our proposed technique produces quantitatively superior results on our dataset's validation set compared to prior works, and produces convincing qualitative relighting results on a dataset of hundreds of real-world cellphone portraits. Because our technique can produce a 640 × 640 image in only 160 milliseconds, it may enable interactive user-facing photographic applications in the future.
Optimal multiple importance sampling
Authors: Kondapaneni Ivo , Vevoda Petr , Grittmann Pascal , Skřivan Tomáš , Slusallek Philipp , Křivánek Jaroslav
Published: July 2019

Multiple Importance Sampling (MIS) is a key technique for achieving robustness of Monte Carlo estimators in computer graphics and other fields. We derive optimal weighting functions for MIS that provably minimize the variance of an MIS estimator, given a set of sampling techniques. We show that the resulting variance reduction over the balance heuristic can be higher than predicted by the variance bounds derived by Veach and Guibas, who assumed only non-negative weights in their proof. We theoretically analyze the variance of the optimal MIS weights and show the relation to the variance of the balance heuristic. Furthermore, we establish a connection between the new weighting functions and control variates as previously applied to mixture sampling. We apply the new optimal weights to integration problems in light transport and show that they allow for new design considerations when choosing the appropriate sampling techniques for a given integration problem.
Ellipsoidal path connections for time-gated rendering
Authors: Pediredla Adithya , Veeraraghavan Ashok , Gkioulekas Ioannis
Published: July 2019

During the last decade, we have been witnessing the continued development of new time-of-flight imaging devices, and their increased use in numerous and varied applications. However, physics-based rendering techniques that can accurately simulate these devices are still lacking: while existing algorithms are adequate for certain tasks, such as simulating transient cameras, they are very inefficient for simulating time-gated cameras because of the large number of wasted path samples. We take steps towards addressing these deficiencies, by introducing a procedure for efficiently sampling paths with a predetermined length, and incorporating it within rendering frameworks tailored towards simulating time-gated imaging. We use our open-source implementation of the above to empirically demonstrate improved rendering performance in a variety of applications, including simulating proximity sensors, imaging through occlusions, depth-selective cameras, transient imaging in dynamic scenes, and non-line-of-sight imaging.
Parametrization quantization with free boundaries for trimmed quad meshing
Authors: Lyon Max , Campen Marcel , Bommes David , Kobbelt Leif
Published: July 2019

The generation of quad meshes based on surface parametrization techniques has proven to be a versatile approach. These techniques quantize an initial seamless parametrization so as to obtain an integer grid map implying a pure quad mesh. State-of-the-art methods following this approach have to assume that the surface to be meshed either has no boundary, or has a boundary which the resulting mesh is supposed to be aligned to. In a variety of applications this is not desirable and non-boundary-aligned meshes or grid-parametrizations are preferred. We thus present a technique to robustly generate integer grid maps which are either boundary-aligned, non-boundary-aligned, or partially boundary-aligned, just as required by different applications. We thereby generalize previous work to this broader setting. This enables the reliable generation of trimmed quad meshes with partial elements along the boundary, preferable in various scenarios, from tiled texturing over design and modeling to fabrication and architecture, due to fewer constraints and hence higher overall mesh quality and other benefits in terms of aesthetics and flexibility.
SPOT
Authors: Bonneel Nicolas , Coeurjolly David
Published: July 2019

Optimal transport research has surged in the last decade with wide applications in computer graphics. In most cases, however, it has focused on the special case of the so-called "balanced" optimal transport problem, that is, the problem of optimally matching positive measures of equal total mass. While this approach is suitable for handling probability distributions as their total mass is always equal to one, it precludes other applications manipulating disparate measures. Our paper proposes a fast approach to the optimal transport of constant distributions supported on point sets of different cardinality via one-dimensional slices. This leads to one-dimensional partial assignment problems akin to alignment problems encountered in genomics or text comparison. Contrary to one-dimensional balanced optimal transport that leads to a trivial linear-time algorithm, such partial optimal transport, even in 1-d, has not seen any closed-form solution nor very efficient algorithms to date. We provide the first efficient 1-d partial optimal transport solver. Along with a quasilinear time problem decomposition algorithm, it solves 1-d assignment problems consisting of up to millions of Dirac distributions within fractions of a second in parallel. We handle higher dimensional problems via a slicing approach, and further extend the popular iterative closest point algorithm using optimal transport - an algorithm we call
Fast Iterative Sliced Transport.
We illustrate our method on computer graphics applications such a color transfer and point cloud registration.
Handheld multi-frame super-resolution
Authors: Wronski Bartlomiej , Garcia-Dorado Ignacio , Ernst Manfred , Kelly Damien , Krainin Michael , Liang Chia-Kai , Levoy Marc , Milanfar Peyman
Published: July 2019

Compared to DSLR cameras, smartphone cameras have smaller sensors, which limits their spatial resolution; smaller apertures, which limits their light gathering ability; and smaller pixels, which reduces their signal-to-noise ratio. The use of color filter arrays (CFAs) requires demosaicing, which further degrades resolution. In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images. We harness natural hand tremor, typical in handheld photography, to acquire a burst of raw frames with small offsets. These frames are then aligned and merged to form a single image with red, green, and blue values at every pixel site. This approach, which includes no explicit demosaicing step, serves to both increase image resolution and boost signal to noise ratio. Our algorithm is robust to challenging scene conditions: local motion, occlusion, or scene changes. It runs at 100 milliseconds per 12-megapixel RAW input burst frame on mass-produced mobile phones. Specifically, the algorithm is the basis of the
Super-Res Zoom
feature, as well as the default merge method in
Night Sight
mode (whether zooming or not) on Google's flagship phone.
Deep reflectance fields
Authors: Meka Abhimitra , Häne Christian , Pandey Rohit , Zollhöfer Michael , Fanello Sean , Fyffe Graham , Kowdle Adarsh , Yu Xueming , Busch Jay , Dourgarian Jason , Denny Peter , Bouaziz Sofien , Lincoln Peter , Whalen Matt , Harvey Geoff , Taylor Jonathan , Izadi Shahram , Tagliasacchi Andrea , Debevec Paul , Theobalt Christian , Valentin Julien , Rhemann Christoph
Published: July 2019

We present a novel technique to relight images of human faces by learning a model of facial reflectance from a database of 4D reflectance field data of several subjects in a variety of expressions and viewpoints. Using our learned model, a face can be relit in arbitrary illumination environments using only two original images recorded under spherical color gradient illumination. The output of our deep network indicates that the color gradient images contain the information needed to estimate the full 4D reflectance field, including specular reflections and high frequency details. While capturing spherical color gradient illumination still requires a special lighting setup, reduction to just two illumination conditions allows the technique to be applied to dynamic facial performance capture. We show side-by-side comparisons which demonstrate that the proposed system outperforms the state-of-the-art techniques in both realism and speed.
Text-based editing of talking-head video
Authors: Fried Ohad , Tewari Ayush , Zollhöfer Michael , Finkelstein Adam , Shechtman Eli , Goldman Dan B , Genova Kyle , Jin Zeyu , Theobalt Christian , Agrawala Maneesh
Published: July 2019

Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis.
A symmetric objective function for ICP
Authors: Rusinkiewicz Szymon
Published: July 2019

The Iterative Closest Point (ICP) algorithm, commonly used for alignment of 3D models, has previously been defined using either a point-to-point or point-to-plane objective. Alternatively, researchers have proposed computationally-expensive methods that directly minimize the distance function between surfaces. We introduce a new symmetrized objective function that achieves the simplicity and computational efficiency of point-to-plane optimization, while yielding improved convergence speed and a wider convergence basin. In addition, we present a linearization of the objective that is exact in the case of exact correspondences. We experimentally demonstrate the improved speed and convergence basin of the symmetric objective, on both smooth models and challenging cases involving noise and partial overlap.
Synthetic silviculture
Authors: Makowski Miłosz , Hädrich Torsten , Scheffczyk Jan , Michels Dominik L. , Pirk Sören , Pałubicki Wojtek
Published: July 2019

Due to the enormous amount of detail and the interplay of various biological phenomena, modeling realistic ecosystems of trees and other plants is a challenging and open problem. Previous research on modeling plant ecologies has focused on representations to handle this complexity, mostly through geometric simplifications, such as points or billboards. In this paper we describe a multi-scale method to design large-scale ecosystems with individual plants that are realistically modeled and faithfully capture biological features, such as growth, plant interactions, different types of tropism, and the competition for resources. Our approach is based on leveraging inter- and intra-plant self-similarities for efficiently modeling plant geometry. We focus on the interactive design of plant ecosystems of up to 500K plants, while adhering to biological priors known in forestry and botany research. The introduced parameter space supports modeling properties of nine distinct plant ecologies while each plant is represented as a 3D surface mesh. The capabilities of our framework are illustrated through numerous models of forests, individual plants, and validations.
Photon surfaces for robust, unbiased volumetric density estimation
Authors: Deng Xi , Jiao Shaojie , Bitterli Benedikt , Jarosz Wojciech
Published: July 2019

We generalize photon planes to
photon surfaces
: a new family of unbiased volumetric density estimators which we combine using multiple importance sampling. To derive our new estimators, we start with the extended path integral which duplicates the vertex at the end of the camera and photon subpaths and couples them using a blurring kernel. To make our formulation unbiased, however, we use a delta kernel to couple these two end points. Unfortunately, sampling the resulting singular integral using Monte Carlo is impossible since the probability of generating a contributing light path by independently sampling the two subpaths is zero. Our key insight is that we can eliminate the delta kernel and make Monte Carlo estimation practical by integrating any three dimensions analytically, and integrating only the remaining dimensions using Monte Carlo. We demonstrate the practicality of this approach by instantiating a collection of estimators which analytically integrate the distance along the camera ray and two arbitrary sampling dimensions along the photon subpath (e.g., distance, direction, surface area). This generalizes photon planes to curved "photon surfaces", including new "photon cone", "photon cylinder", "photon sphere", and multiple new "photon plane" estimators. These estimators allow us to handle light paths not supported by photon planes, including single scattering, and surface-to-media transport. More importantly, since our estimators have complementary strengths due to analytically integrating different dimensions of the path integral, we can combine them using multiple importance sampling. This combination mitigates singularities present in individual estimators, substantially reducing variance while remaining fully unbiased. We demonstrate our improved estimators on a number of scenes containing homogeneous media with highly anisotropic phase functions, accelerating both multiple scattering and single scattering compared to prior techniques.
Weaving geodesic foliations
Authors: Vekhter Josh , Zhuo Jiacheng , Fandino Luisa F Gil , Huang Qixing , Vouga Etienne
Published: July 2019

We study discrete geodesic foliations of surfaces---foliations whose leaves are all approximately geodesic curves---and develop several new variational algorithms for computing such foliations. Our key insight is a relaxation of vector field integrability in the discrete setting, which allows us to optimize for curl-free unit vector fields that remain well-defined near singularities and robustly recover a scalar function whose gradient is well aligned to these fields. We then connect the physics governing surfaces woven out of thin ribbons to the geometry of geodesic foliations, and present a design and fabrication pipeline for approximating surfaces of arbitrary geometry and topology by triaxially-woven structures, where the ribbon layout is determined by a geodesic foliation on a sixfold branched cover of the input surface. We validate the effectiveness of our pipeline on a variety of simulated and fabricated woven designs, including an example for readers to try at home.
Physically-based statistical simulation of rain sound
Authors: Liu Shiguang , Cheng Haonan , Tong Yiying
Published: July 2019

A typical rainfall scenario contains tens of thousands of dynamic sound sources. A characteristic of the large-scale scene is the strong randomness in raindrop distribution, which makes it notoriously expensive to synthesize such sounds with purely physical methods. Moreover, the raindrops hitting different surfaces (liquid or various solids) can emit distinct sounds, for which prior methods with unified impact sound models are ill-suited.

In this paper, we present a physically-based statistical simulation method to synthesize realistic rain sound, which respects surface materials. We first model the raindrop sound with two mechanisms, namely the initial impact and the subsequent pulsation of entrained bubbles. Then we generate material sound textures (MSTs) based on a specially designed signal decomposition and reconstruction model. This allows us to distinguish liquid surface with bubble sound and different solid surfaces with MSTs. Furthermore, we build a basic rain sound (BR-sound) bank with the proposed raindrop sound clustering method based on a statistical model, and design a sound source activator for simulating spatial propagation in an efficient manner. This novel method drastically decreases the computational cost while producing convincing sound results. Various experiments demonstrate the effectiveness of our sound simulation model.
Whole-Cell Models and Simulations in Molecular Detail
Authors: Feig Michael , Sugita Yuji
Published: July 2019

Comprehensive data about the composition and structure of cellular components have enabled the construction of quantitative whole-cell models. While kinetic network–type models have been established, it is also becoming possible to build physical, molecular-level models of cellular environments. This review outlines challenges in constructing and simulating such models and discusses near- and long-term opportunities for developing physical whole-cell models that can connect molecular structure with biological function.
Light-Sheet Microscopy and Its Potential for Understanding Developmental Processes
Authors: Wan Yinan , McDole Katie , Keller Philipp J.
Published: July 2019

The ability to visualize and quantitatively measure dynamic biological processes in vivo and at high spatiotemporal resolution is of fundamental importance to experimental investigations in developmental biology. Light-sheet microscopy is particularly well suited to providing such data, since it offers exceptionally high imaging speed and good spatial resolution while minimizing light-induced damage to the specimen. We review core principles and recent advances in light-sheet microscopy, with a focus on concepts and implementations relevant for applications in developmental biology. We discuss how light-sheet microcopy has helped advance our understanding of developmental processes from single-molecule to whole-organism studies, assess the potential for synergies with other state-of-the-art technologies, and introduce methods for computational image and data analysis. Finally, we explore the future trajectory of light-sheet microscopy, discuss key efforts to disseminate new light-sheet technology, and identify exciting opportunities for further advances.
Beyond Reproductive Isolation: Demographic Controls on the Speciation Process
Authors: Harvey Michael G. , Singhal Sonal , Rabosky Daniel L.
Published: July 2019

Studies of speciation typically investigate the evolution of reproductive isolation between populations, but several other processes can serve as key steps limiting the formation of species. In particular, the probability of successful speciation can be influenced by factors that affect the frequency with which population isolates form as well as their persistence through time. We suggest that population isolation and persistence have an inherently spatial dimension that can be profitably studied using a conceptual framework drawn from metapopulation ecology. We discuss models of speciation that incorporate demographic processes and highlight the need for a broader application of phylogenetic comparative approaches to evaluate the general importance of population isolation, persistence, and reproductive isolation in speciation. We review diverse and nontraditional data sources that can be leveraged to study isolation and persistence in a comparative framework. This incorporation of spatial demographic information facilitates the integration of perspectives on speciation across disciplines and timescales.