bluuebunny/crossref_metadata_2025 huggingface dataset:
Paraneoplastic cochleovestibulopathy: clinical presentations, oncological and serological associations
Authors: Hammami M Bakri , Eggers Scott D Z , Madhavan Ajay , Montalvo Mayra J , Pittock Sean J , Dubey Divyanshu
Published: July 2021

Objective

Cochleovestibulopathy is a distinguishable paraneoplastic phenotype. In this study, we evaluate clinical presentation, serological/cancer associations and outcomes of paraneoplastic cochleovestibulopathy.

Methods

Retrospective chart review of patients with hearing impairment and/or vestibulopathy who underwent serological evaluations for paraneoplastic antibodies between January 2007 and February 2021 was performed.

Results

Twenty-six patients were identified (men, n=23; median age, 45 years, range: 28–70). Biomarkers detected included: KLHL11-IgG‌ ‌(n=20,‌ ‌77% (coexisting LUZP4-IgG, n=8)),‌ ‌‌ANNA1-IgG‌ ‌ ‌(n=3,‌ ‌12%),‌ ‌amphiphysin-IgG‌‌ ‌(n=2,‌ ‌8%)‌ ‌and‌ ‌LUZP4-IgG‌‌ ‌(n=1,‌ ‌4%). Most common neoplastic association was ‌testicular‌/‌extra-testicular‌ ‌seminoma‌ ‌ (n=13,‌ ‌50%).‌‌ Hearing‌ impairment (bilateral, 62%) was ‌present‌ ‌in‌ ‌all‌ ‌patients.‌ ‌Fifteen patients (58%) had cochleovestibular dysfunction as their initial presentation before rhombencephalitis/encephalomyelitis manifestations (hearing loss, four; acute vertigo, eight; both, three). ‌Brain‌ ‌MRI‌ ‌demonstrated‌ ‌internal‌ ‌auditory‌ ‌canal‌ ‌enhancement‌ ‌in‌ ‌four ‌patients.‌ Audiometry commonly revealed severe-profound bilateral sensorineural hearing loss. Most patients ‌had‌ a refractory course ‌despite‌ ‌immunotherapy‌ ‌and/or‌ ‌cancer‌ ‌treatment‌.

Conclusion

Cochleovestibulopathy commonly presents with rapidly progressive bilateral hearing loss and/or acute vertigo. However, in some patients, these symptoms present along with or following brainstem/cerebellar manifestations. KLHL11-IgG and seminoma are the most common serological and cancer associations, respectively. Recognition of this phenotype may aid in earlier diagnosis of paraneoplastic autoimmunity and associated cancer.
Gradation characteristics-based interlayer mixture design method for enhanced rutting resistance of asphalt pavements
Authors: Park Bongsuk , Cocconcelli Cristian , Chun Sanghyun
Published: July 2021

The major role of interlayer mixtures is to mitigate reflective cracking by absorbing or dissipating concentrated stress, and relatively low-stiffness materials are typically used. However, there is a concern that interlayer mixtures may increase the risk of rutting because of these low-stiffness materials. The dominant aggregate size range (DASR) porosity has been successfully applied for structural mixtures to ensure enhanced rutting performance. This study mainly focused on developing new DASR porosity requirements for interlayer mixtures that ensures acceptable rutting performance in the mix design phase. Ten interlayer mixtures with a broad range of DASR porosities were evaluated using the asphalt pavement analyzer test. Our results indicated that the gradation characteristics of interlayer mixtures played an important role in rutting performance. Also, a relationship between DASR porosity and the rutting potential of interlayer mixtures was identified that resulted in the establishment of the preliminary DASR porosity requirements.
Phytotoxin of rice aggregate sheath spot pathogen Rhizoctonia oryzae-sativae and its biological activities
Authors: Wang Xiaoyu , Wang Aimin , Chen Zhiyi , Wei Lihui
Published: July 2021

Rice aggregate sheath spot disease occurs in many countries and causes serious yield losses. In China, the disease-causing fungus Rhizoctonia oryzae-sativae was reported in 1985, and since then, it has rarely been reported in major rice-growing areas after almost 30 years. Compared with Rhizoctonia solani, R. oryzae-sativae has a significantly different physiological morphology and growth status, although both fungi affect rice leaves in very similar ways. The optimum temperature for the suitable growth of R. oryzae-sativae is 31 °C, which is consistent with previous reports. We extracted phytotoxins from R. oryzae-sativae and analyzed its biological activity via the detached leaf and radicle inhibition methods. Rhizoctonia solani and R. oryzae-sativae exhibit differences in terms of pathogenicity and toxin activity, which indicates that these fungi may produce different toxin components. Based on gas chromatography–mass spectrometry data, esters, phenols, and other components were present in the crude toxin extract of R. oryzae-sativae. Our research provides a new method for studying the phytotoxins of R. oryzae-sativae. However, further studies are needed to elucidate the pathogenic mechanisms responsible for aggregate sheath spot disease in rice.
Angiogenesis effects of 4-methoxy benzyl alcohol on cerebral ischemia-reperfusion injury via regulation of VEGF-Ang/Tie2 balance
Authors: He Fangyan , Ma Chenjing , Feng Jin , Li Xiufang , Xia Shuangli , Lin Qing , Dai Rong
Published: July 2021

Angiogenesis facilitates the formation of microvascular networks and promotes neurological deficit recovery after cerebral ischemia-reperfusion injury (CIRI). This study investigated the angiogenesis effects of 4-methoxy benzyl alcohol (4-MA) on CIRI. The angiogenesis effects of 4-MA and the potential underlying mechanisms were assessed based on a middle cerebral artery occlusion/reperfusion (MCAO/R) rat model and a hind limb ischemic (HLI) mouse model. Immunofluorescence was conducted to detect microvessel density, and Western blotting and polymerase chain reaction were performed to determine the expression of angiogenesis-promoting factors. In addition, we investigated whether the angiogenesis effects of 4-MA caused damage to the blood–brain barrier (BBB). After treatment with 4-MA (20 mg/kg) for 7 days, the neurological deficits recovered and microvessel density in the cerebral cortex increased in the MCAO/R rats. Additionally, 4-MA also regulated the expression of angiogenesis factors, with an increase in vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor 2 (VEGFR-2) expression and a decrease in angiopoietin 1 (Ang-1), Ang-2, and Tie-2 expression in both MCAO/R rats and HLI mice. Moreover, 4-MA increased the expression of angiogenesis-promoting factors without exacerbating BBB cascade damage in MCAO/R rats. Our results indicated that 4-MA may contribute to the formation of microvascular networks, thus promoting neurological deficit recovery after CIRI.
Bound states for massive Dirac fermions in graphene in a magnetic step field
Authors: Dell’Anna L. , Alidoust Ghatar A. , Jahani D.
Published: July 2021

We calculate the spectrum of massive Dirac fermions in graphene in the presence of an inhomogeneous magnetic field modeled by a step function. We find an analytical universal relation between the bandwidths and the propagating velocities of the modes at the border of the magnetic region, showing how, by tuning the mass term, one can control the speed of these traveling edge states.

Graphic Abstract
Construction of inflationary scenarios with the Gauss–Bonnet term and nonminimal coupling
Authors: Pozdeeva Ekaterina O. , Vernov Sergey Yu.
Published: July 2021

Inflationary models with a scalar field nonminimally coupled both with the Ricci scalar and with the Gauss–Bonnet term are studied. We propose the way of generalization of inflationary scenarios with the Gauss–Bonnet term and a scalar field minimally coupled with the Ricci scalar to the corresponding scenarios with a scalar field nonminimally coupled with the Ricci scalar. Using the effective potential, we construct a set of models with the same values of the scalar spectral index $$n_s$$

n
s

and the amplitude of the scalar perturbations $$A_s$$

A
s

and different values of the tensor-to-scalar ratio r.
Boer–Mulders function of the pion and pretzelosity distribution of the proton in the polarized pion-proton Drell–Yan process at COMPASS
Authors: Liu Xiaonan , Ma Bo-Qiang
Published: July 2021

We present a phenomenological analysis of the $$q_{\text {T}}$$

q
T

-weighted transverse spin dependent azimuthal asymmetry recently measured by the COMPASS Collaboration in polarized pion-proton Drell–Yan process. In the kinematical regimes explored by experiments, we consider the leading-twist contributions from the Boer–Mulders distribution functions $$h_{1}^{\perp }(x,k_{\text {T}}^{2})$$

h

1

⊥

(
x
,

k

T

2

)

of both the pion and the proton, the transversity distribution $$h_{1}(x,k_{\text {T}}^{2})$$

h
1

(
x
,

k

T

2

)

and the pretzelosity distribution $$h_{1\text {T}}^{\perp }(x,k_{\text {T}}^{2})$$

h

1
T

⊥

(
x
,

k

T

2

)

of the proton, with the unpolarized transverse-momentum-dependent distribution function $$f_{1}(x,k_{\text {T}}^{2})$$

f
1

(
x
,

k

T

2

)

of the proton being also involved in the calculation. By comparing the data reported by the COMPASS Collaboration with theoretical calculated results, we find that the COMPASS measurements represent the first experimental evidence of the Boer–Mulders effect in polarized Drell–Yan process. We also test the universality between proton and pion Boer–Mulders functions.
Perspective on Tsallis statistics for nuclear and particle physics
Authors: Kapusta Joseph I.
Published: July 2021

This is a concise introduction to the topic of nonextensive Tsallis statistics meant especially for those interested in its relation to high-energy proton–proton, proton–nucleus and nucleus–nucleus collisions. The three types of Tsallis statistics are reviewed. Only one of them is consistent with the fundamental hypothesis of equilibrium statistical mechanics. The single-particle distributions associated with it, namely Boltzmann, Fermi–Dirac and Bose–Einstein, are derived. These are not equilibrium solutions to the conventional Boltzmann transport equation which must be modified in a rather nonintuitive manner for them to be so. Nevertheless, the Boltzmann limit of the Tsallis distribution is extremely efficient in representing a wide variety of single-particle distributions in high-energy proton–proton, proton–nucleus and nucleus–nucleus collisions with only three parameters, one of them being the so-called nonextensitivity parameter [Formula: see text]. This distribution interpolates between an exponential at low transverse energy, reflecting thermal equilibrium, to a power law at high transverse energy, reflecting the asymptotic freedom of Quantum Chromodynamics (QCD). It should not be viewed as a fundamental new parameter representing nonextensive behavior in these collisions.
Application of the Traveling Salesman Problem in Generating an Optimized Collision-Free Tool Path for CNC Drilling
Authors: Khodabakhshi Z. , Hosseini A. , Ghandehariun A.
Published: July 2021

In drilling, the tool path is usually generated according to the workpiece geometry and the arrangement of holes. To perform drilling, majority of Computer-Aided Manufacturing (CAM) software offer a set of predefined drilling strategies. In this context, tool traveling time between the holes is considered as a non-value-adding movement and thus must be minimized. However, generating the optimum tool path with the shortest possible traveling distance in the presence of obstacles received little attention and therefore, CAM-generated tool paths are not necessarily optimum. This paper introduces a new algorithm based upon Traveling Salesman Problem (TSP) to minimize the tool path length while considering collision avoidance. The developed optimization model considers multiple constraints, including the location of tool origin and the presence of obstacles along the tool path, to generate a collision-free trajectory with minimum length. Performance of the proposed model was compared to the tool path generated by HSMWorks CAM software and the results confirmed the ability of the algorithm in generating an optimum or near-optimum, collision-free tool path for real-world drilling applications with a minimal computational time.
Autonomous Mission Management Based Nonlinear Flight Control Design for a Class of Hybrid Unmanned Aerial Vehicles
Authors: Peng Kemao
Published: July 2021

In this paper, a nonlinear flight control law is designed for a hybrid unmanned aerial vehicle (UAV) to achieve the advanced flight performances with the autonomous mission management (AMM). The hybrid UAV is capable of hovering like quadrotors and maneuvering as fixed-wing aircraft. The main idea is to design the flight control laws in modules. Those modules are organized online by the autonomous mission management. Such online organization will improve the UAV autonomy. One of the challenges is to execute the transition flight between the rotary-wing and fixed-wing modes. The resulting closed-loop system with the designed flight control law is verified in simulation and the simulation results demonstrate that the resulting closed-loop system can successfully complete the designated flight missions including the transition flight between the rotary-wing and fixed-wing modes.
Ascent Trajectory Optimization for Air-Breathing Hypersonic Vehicles Based on IGS-MPSP
Authors: Liu Lei , He Qianwei , Wang Bo , Fu Wenzhe , Cheng Zhongtao , Wang Yongji
Published: July 2021

This paper proposes an improved Generalized Quasi-Spectral Model Predictive Static Programming (GS-MPSP) algorithm for the ascent trajectory optimization for hypersonic vehicles in a complex flight environment. The proposed method guarantees the satisfaction of constraints related to the state and control vector while retaining its high computational efficiency. The spectral representation technique is used to describe the control variables, which reduces the number of decision variables and makes the control input smooth enough. Through Taylor expansion, the constraints are transformed into an inequality containing only decision variables, such that it can be added into GS-MPSP framework. By Gauss quadrature collocation method, only a few collocation points are needed to solve the sensitivity matrix, which greatly accelerates the calculation. Subsequently, the analytical expression is obtained by combining the static optimization with the penalty function method. Finally, the simulation results demonstrate that the proposed improved GS-MPSP algorithm can achieve both high computational efficiency and high terminal precision under the constraints.
Query Expansion for Transliterated Text Retrieval
Authors: Prabhakar Dinesh Kumar , Pal Sukomal , Kumar Chiranjeev
Published: July 2021

With Web 2.0, there has been exponential growth in the number of Web users and the volume of Web content. Most of these users are not only consumers of the information but also generators of it. People express themselves here in colloquial languages, but using Roman script (transliteration). These texts are mostly informal and casual, and therefore seldom follow grammar rules. Also, there does not exist any prescribed set of spelling rules in transliterated text. This freedom leads to large-scale spelling variations, which is a major challenge in mixed script information processing. This article studies different existing phonetic algorithms to handle the issue of spelling variation, points out the limitations of them, and proposes a novel phonetic encoding approach with two different flavors in the light of Hindi transliteration. Experiments performed over Hindi song lyrics retrieval in mixed script domain with three different retrieval models show that proposed approaches outperform the existing techniques in a majority of the cases (sometimes statistically significantly) for a number of metrics like nDCG@1, nDCG@5, nDCG@10, MAP, MRR, and Recall.
Video recoloring via spatial-temporal geometric palettes
Authors: Du Zheng-Jun , Lei Kai-Xiang , Xu Kun , Tan Jianchao , Gingold Yotam
Published: July 2021

Color correction and color grading are important steps in film production. Recent palette-based approaches to image recoloring have shown that a small set of representative colors provide an intuitive set of handles for color adjustment. However, a single, static palette cannot represent the time-varying colors in a video. We introduce a spatial-temporal geometry-based approach to video recoloring. Specifically, its core is a 4D skew polytope with a few vertices that approximately encloses the video pixels in color and time, which implicitly defines time-varying palettes through slicing of the 4D skew polytope at specific time values. Our geometric palette is compact, descriptive, and provides a correspondence between colors throughout the video, including topological changes when colors merge or split. Experiments show that our method produces natural, artifact-free recoloring.
SANM
Authors: Jia Kai
Published: July 2021

Solving nonlinear systems is an important problem. Numerical continuation methods efficiently solve certain nonlinear systems. The Asymptotic Numerical Method (ANM) is a powerful continuation method that usually converges faster than Newtonian methods. ANM explores the landscape of the function by following a parameterized solution curve approximated with a high-order power series. Although ANM has successfully solved a few graphics and engineering problems, prior to our work, applying ANM to new problems required significant effort because the standard ANM assumes quadratic functions, while manually deriving the power series expansion for nonquadratic systems is a tedious and challenging task.

This paper presents a novel solver, SANM, that applies ANM to solve symbolically represented nonlinear systems. SANM solves such systems in a fully automated manner. SANM also extends ANM to support many nonquadratic operators, including intricate ones such as singular value decomposition. Furthermore, SANM generalizes ANM to support the implicit homotopy form. Moreover, SANM achieves high computing performance via optimized system design and implementation.

We deploy SANM to solve forward and inverse elastic force equilibrium problems and controlled mesh deformation problems with a few constitutive models. Our results show that SANM converges faster than Newtonian solvers, requires little programming effort for new problems, and delivers comparable or better performance than a hand-coded, specialized ANM solver. While we demonstrate on mesh deformation problems, SANM is generic and potentially applicable to many tasks.
DeepFaceEditing
Authors: Chen Shu-Yu , Liu Feng-Lin , Lai Yu-Kun , Rosin Paul L. , Li Chunpeng , Fu Hongbo , Gao Lin
Published: July 2021

Recent facial image synthesis methods have been mainly based on conditional generative models. Sketch-based conditions can effectively describe the geometry of faces, including the contours of facial components, hair structures, as well as salient edges (e.g., wrinkles) on face surfaces but lack effective control of appearance, which is influenced by color, material, lighting condition, etc. To have more control of generated results, one possible approach is to apply existing disentangling works to disentangle face images into geometry and appearance representations. However, existing disentangling methods are not optimized for human face editing, and cannot achieve fine control of facial details such as wrinkles. To address this issue, we propose DeepFaceEditing, a structured disentanglement framework specifically designed for face images to support face generation and editing with disentangled control of geometry and appearance. We adopt a local-to-global approach to incorporate the face domain knowledge: local component images are decomposed into geometry and appearance representations, which are fused consistently using a global fusion module to improve generation quality. We exploit sketches to assist in extracting a better geometry representation, which also supports intuitive geometry editing via sketching. The resulting method can either extract the geometry and appearance representations from face images, or directly extract the geometry representation from face sketches. Such representations allow users to easily edit and synthesize face images, with decoupled control of their geometry and appearance. Both qualitative and quantitative evaluations show the superior detail and appearance control abilities of our method compared to state-of-the-art methods.
Surface multigrid via intrinsic prolongation
Authors: Liu Hsueh-Ti Derek , Zhang Jiayi Eris , Ben-Chen Mirela , Jacobson Alec
Published: July 2021

This paper introduces a novel geometric multigrid solver for unstructured curved surfaces. Multigrid methods are highly efficient iterative methods for solving systems of linear equations. Despite the success in solving problems defined on structured domains, generalizing multigrid to unstructured curved domains remains a challenging problem. The critical missing ingredient is a prolongation operator to transfer functions across different multigrid levels. We propose a novel method for computing the prolongation for triangulated surfaces based on intrinsic geometry, enabling an efficient geometric multigrid solver for curved surfaces. Our surface multigrid solver achieves better convergence than existing multigrid methods. Compared to direct solvers, our solver is orders of magnitude faster. We evaluate our method on many geometry processing applications and a wide variety of complex shapes with and without boundaries. By simply replacing the direct solver, we upgrade existing algorithms to interactive frame rates, and shift the computational bottleneck away from solving linear systems.
StrokeStrip
Authors: Van Mossel Dave Pagurek , Liu Chenxi , Vining Nicholas , Bessmeltsev Mikhail , Sheffer Alla
Published: July 2021

When creating freeform drawings, artists routinely employ clusters of overdrawn strokes to convey intended, aggregate curves. The ability to algorithmically fit these intended curves to their corresponding clusters is central to many applications that use artist drawings as inputs. However, while human observers effortlessly envision the intended curves given stroke clusters as input, existing fitting algorithms lack robustness and frequently fail when presented with input stroke clusters with non-trivial geometry or topology. We present
StrokeStrip
, a new and robust method for fitting intended curves to vector-format stroke clusters. Our method generates fitting outputs consistent with viewer expectations across a vast range of input stroke cluster configurations. We observe that viewers perceive stroke clusters as continuous, varying-width
strips
whose paths are described by the intended curves. An arc length parameterization of these strips defines a natural mapping from a strip to its path. We recast the curve fitting problem as one of parameterizing the cluster strokes using a
joint
1D parameterization that is the restriction of the natural arc length parameterization of this strip to the strokes in the cluster. We simultaneously compute the joint cluster parameterization and implicitly reconstruct the
a priori
unknown strip geometry by solving a variational problem using a discrete-continuous optimization framework. We use this parameterization to compute parametric aggregate curves whose shape reflects the geometric properties of the cluster strokes at the corresponding isovalues. We demonstrate StrokeStrip outputs to be significantly better aligned with observer preferences compared to those of prior art; in a perceptual study, viewers preferred our fitting outputs by a factor of 12:1 compared to alternatives. We further validate our algorithmic choices via a range of ablation studies; extend our framework to raster data; and illustrate applications that benefit from the parameterizations produced.
A generic framework for physical light transport
Authors: Steinberg Shlomi , Yan Ling-Qi
Published: July 2021

Physically accurate rendering often calls for taking the wave nature of light into consideration. In computer graphics, this is done almost exclusively locally, i.e. on a micrometre scale where the diffractive phenomena arise. However, the statistical properties of light, that dictate its coherence characteristics and its capacity to give rise to wave interference effects, evolve globally: these properties change on, e.g., interaction with a surface, diffusion by participating media and simply by propagation. In this paper, we derive the first global light transport framework that is able to account for these properties of light and, therefore, is fully consistent with Maxwell's electromagnetic theory. We show that our framework is a generalization of the classical, radiometry-based light transport---prominent in computer graphics---and retains some of its attractive properties. Finally, as a proof of concept, we apply the presented framework to a few practical problems in rendering and validate against well-studied methods in optics.
Interactive Monte Carlo denoising using affinity of neural features
Authors: Işık Mustafa , Mullia Krishna , Fisher Matthew , Eisenmann Jonathan , Gharbi Michaël
Published: July 2021

High-quality denoising of Monte Carlo low-sample renderings remains a critical challenge for practical interactive ray tracing. We present a new learning-based denoiser that achieves state-of-the-art quality and runs at interactive rates. Our model processes individual path-traced
samples
with a lightweight neural network to extract per-pixel feature vectors. The rest of our pipeline operates in pixel space. We define a novel pairwise affinity over the features in a pixel neighborhood, from which we assemble dilated spatial kernels to filter the noisy radiance. Our denoiser is temporally stable thanks to two mechanisms. First, we keep a running average of the noisy radiance and intermediate features, using a per-pixel recursive filter with learned weights. Second, we use a small temporal kernel based on the pairwise affinity between features of consecutive frames. Our experiments show our new affinities lead to higher quality outputs than techniques with comparable computational costs, and better high-frequency details than kernel-predicting approaches. Our model matches or outperfoms state-of-the-art offline denoisers in the low-sample count regime (2--8 samples per pixel), and runs at interactive frame rates at 1080p resolution.
WireRoom
Authors: Yang Zhijin , Xu Pengfei , Fu Hongbo , Huang Hui
Published: July 2021

We present
WireRoom
, a computational framework for the intelligent design of abstract 3D wire art to depict a given 3D model. Our algorithm generates a set of 3D wire shapes from the 3D model with informative, visually pleasing, and concise structures. It is achieved by solving a dynamic travelling salesman problem on the surface of the 3D model with a multi-path expansion approach. We introduce a novel explorative computational design procedure by taking the generated wire shapes as candidates, avoiding manual design of the wire shape structure. We compare our algorithm with a baseline method and conduct a user study to investigate the usability of the framework and the quality of the produced wire shapes. The results of the comparison and user study confirm that our framework is effective for producing informative, visually pleasing, and concise wire shapes.
Path replay backpropagation
Authors: Vicini Delio , Speierer Sébastien , Jakob Wenzel
Published: July 2021

Differentiable physically-based rendering has become an indispensable tool for solving inverse problems involving light. Most applications in this area jointly optimize a large set of scene parameters to minimize an objective function, in which case reverse-mode differentiation is the method of choice for obtaining parameter gradients.

However, existing techniques that perform the necessary differentiation step suffer from either statistical bias or a prohibitive cost in terms of memory and computation time. For example, standard techniques for automatic differentiation based on program transformation or Wengert tapes lead to impracticably large memory usage when applied to physically-based rendering algorithms. A recently proposed adjoint method by Nimier-David et al. [2020] reduces this to a constant memory footprint, but the computation time for unbiased gradient estimates then becomes quadratic in the number of scattering events along a light path. This is problematic when the scene contains highly scattering materials like participating media.

In this paper, we propose a new unbiased backpropagation algorithm for rendering that only requires constant memory, and whose computation time is linear in the number of scattering events (i.e., just like path tracing). Our approach builds on the invertibility of the local Jacobian at scattering interactions to recover the various quantities needed for reverse-mode differentiation. Our method also extends to specular materials such as smooth dielectrics and conductors that cannot be handled by prior work.
Monte Carlo estimators for differential light transport
Authors: Zeltner Tizian , Speierer Sébastien , Georgiev Iliyan , Jakob Wenzel
Published: July 2021

Physically based differentiable rendering algorithms propagate derivatives through realistic light transport simulations and have applications in diverse areas including inverse reconstruction and machine learning. Recent progress has led to unbiased methods that can simultaneously compute derivatives with respect to millions of parameters. At the same time, elementary properties of these methods remain poorly understood.

Current algorithms for differentiable rendering are constructed by mechanically differentiating a given primal algorithm. While convenient, such an approach is simplistic because it leaves no room for improvement. Differentiation produces major changes in the integrals that occur throughout the rendering process, which indicates that the primal and differential algorithms should be decoupled so that the latter can suitably adapt.

This leads to a large space of possibilities: consider that even the most basic Monte Carlo path tracer already involves several design choices concerning the techniques for sampling materials and emitters, and their combination, e.g. via multiple importance sampling (MIS). Differentiation causes a veritable explosion of this decision tree: should we differentiate only the estimator, or also the sampling technique? Should MIS be applied before or after differentiation? Are specialized derivative sampling strategies of any use? How should visibility-related discontinuities be handled when millions of parameters are differentiated simultaneously? In this paper, we provide a taxonomy and analysis of different estimators for differential light transport to provide intuition about these and related questions.
Discovering diverse athletic jumping strategies
Authors: Yin Zhiqi , Yang Zeshi , Van De Panne Michiel , Yin Kangkang
Published: July 2021

We present a framework that enables the discovery of diverse and natural-looking motion strategies for athletic skills such as the high jump. The strategies are realized as control policies for physics-based characters. Given a task objective and an initial character configuration, the combination of physics simulation and deep reinforcement learning (DRL) provides a suitable starting point for automatic control policy training. To facilitate the learning of realistic human motions, we propose a Pose Variational Autoencoder (P-VAE) to constrain the actions to a subspace of natural poses. In contrast to motion imitation methods, a rich variety of novel strategies can naturally emerge by exploring initial character states through a sample-efficient Bayesian diversity search (BDS) algorithm. A second stage of optimization that encourages novel policies can further enrich the unique strategies discovered. Our method allows for the discovery of diverse and novel strategies for athletic jumping motions such as high jumps and obstacle jumps with no motion examples and less reward engineering than prior work.
DiffAqua
Authors: Ma Pingchuan , Du Tao , Zhang John Z. , Wu Kui , Spielberg Andrew , Katzschmann Robert K. , Matusik Wojciech
Published: July 2021

The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer's geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes
via
optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer's performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.
General virtual sketching framework for vector line art
Authors: Mo Haoran , Simo-Serra Edgar , Gao Chengying , Zou Changqing , Wang Ruomei
Published: July 2021

Vector line art plays an important role in graphic design, however, it is tedious to manually create. We introduce a general framework to produce line drawings from a wide variety of images, by learning a mapping from raster image space to vector image space. Our approach is based on a recurrent neural network that draws the lines one by one. A differentiable rasterization module allows for training with only supervised raster data. We use a dynamic window around a virtual pen while drawing lines, implemented with a proposed aligned cropping and differentiable pasting modules. Furthermore, we develop a stroke regularization loss that encourages the model to use fewer and longer strokes to simplify the resulting vector image. Ablation studies and comparisons with existing methods corroborate the efficiency of our approach which is able to generate visually better results in less computation time, while generalizing better to a diversity of images and applications.
Bijective and coarse high-order tetrahedral meshes
Authors: Jiang Zhongshi , Zhang Ziyi , Hu Yixin , Schneider Teseo , Zorin Denis , Panozzo Daniele
Published: July 2021

We introduce a robust and automatic algorithm to convert linear triangle meshes with feature annotated into coarse tetrahedral meshes with curved elements. Our construction guarantees that the high-order meshes are free of element inversion or self-intersection. A user-specified maximal geometrical error from the input mesh controls the faithfulness of the curved approximation. The boundary of the output mesh is in bijective correspondence to the input, enabling attribute transfer between them, such as boundary conditions for simulations, making our curved mesh an ideal replacement or complement for the original input geometry.

The availability of a bijective shell around the input surface is employed to ensure robust curving, prevent self-intersections, and compute a bijective map between the linear input and curved output surface. As necessary building blocks of our algorithm, we extend the bijective shell formulation to support features and propose a robust approach for boundary-preserving linear tetrahedral meshing.

We demonstrate the robustness and effectiveness of our algorithm by generating high-order meshes for a large collection of complex 3D models.
Modeling and fabrication with specified discrete equivalence classes
Authors: Liu Zhong-Yuan , Zhang Zhan , Zhang Di , Ye Chunyang , Liu Ligang , Fu Xiao-Ming
Published: July 2021

We propose a novel method to model and fabricate shapes using a small set of specified discrete equivalence classes of triangles. The core of our modeling technique is a fabrication-error-driven remeshing algorithm. Given a triangle and a template triangle, which are coplanar and have one-to-one corresponding vertices, we define their similarity error from a manufacturing point of view as follows: the minimizer of the maximum of the three distances between the corresponding pair of vertices concerning a rigid transformation. To compute the similarity error, we convert it into an easy-to-compute form. Then, a greedy remeshing method is developed to optimize the topology and geometry of the input mesh to minimize the fabrication error defined as the maximum similarity error of all triangles. Besides, constraints are enforced to ensure the similarity between input and output shapes and the smoothness of the resulting shapes. Since the fabrication error has been considered during the modeling process, the fabrication process is easy to proceed. To assist users in performing fabrication using common materials and tools manually, we present a straightforward manufacturing solution. The feasibility and practicability of our method are demonstrated over various examples, including seven physical manufacturing models with only nine template triangles.
Learning meaningful controls for fluids
Authors: Chu Mengyu , Thuerey Nils , Seidel Hans-Peter , Theobalt Christian , Zayer Rhaleb
Published: July 2021

While modern fluid simulation methods achieve high-quality simulation results, it is still a big challenge to interpret and control motion from visual quantities, such as the advected marker density. These visual quantities play an important role in user interactions: Being familiar and meaningful to humans, these quantities have a strong correlation with the underlying motion. We propose a novel data-driven conditional adversarial model that solves the challenging and theoretically ill-posed problem of deriving plausible velocity fields from a single frame of a density field. Besides density modifications, our generative model is the first to enable the control of the results using all of the following control modalities: obstacles, physical parameters, kinetic energy, and vorticity. Our method is based on a new conditional generative adversarial neural network that explicitly embeds physical quantities into the learned latent space, and a new cyclic adversarial network design for control disentanglement. We show the high quality and versatile controllability of our results for density-based inference, realistic obstacle interaction, and sensitive responses to modifications of physical parameters, kinetic energy, and vorticity. Code, models, and results can be found at https://github.com/RachelCmy/den2vel.
Multiscale Cholesky preconditioning for ill-conditioned problems
Authors: Chen Jiong , Schäfer Florian , Huang Jin , Desbrun Mathieu
Published: July 2021

Many computer graphics applications boil down to solving sparse systems of linear equations. While the current arsenal of numerical solvers available in various specialized libraries and for different computer architectures often allow efficient and scalable solutions to image processing, modeling and simulation applications, an increasing number of graphics problems face large-scale and ill-conditioned sparse linear systems --- a numerical challenge which typically chokes both direct factorizations (due to high memory requirements) and iterative solvers (because of slow convergence). We propose a novel approach to the efficient preconditioning of such problems which often emerge from the discretization over unstructured meshes of partial differential equations with heterogeneous and anisotropic coefficients. Our numerical approach consists in simply performing a fine-to-coarse ordering and a multiscale sparsity pattern of the degrees of freedom, using which we apply an incomplete Cholesky factorization. By further leveraging supernodes for cache coherence, graph coloring to improve parallelism and partial diagonal shifting to remedy negative pivots, we obtain a preconditioner which, combined with a conjugate gradient solver, far exceeds the performance of existing carefully-engineered libraries for graphics problems involving bad mesh elements and/or high contrast of coefficients. We also back the core concepts behind our simple solver with theoretical foundations linking the recent method of operator-adapted wavelets used in numerical homogenization to the traditional Cholesky factorization of a matrix, providing us with a clear bridge between incomplete Cholesky factorization and multiscale analysis that we leverage numerically.
Learning skeletal articulations with neural blend shapes
Authors: Li Peizhuo , Aberman Kfir , Hanocka Rana , Liu Libin , Sorkine-Hornung Olga , Chen Baoquan
Published: July 2021

Animating a newly designed character using motion capture (mocap) data is a long standing problem in computer animation. A key consideration is the skeletal structure that should correspond to the available mocap data, and the shape deformation in the joint regions, which often requires a tailored, pose-specific refinement. In this work, we develop a neural technique for articulating 3D characters using enveloping with a pre-defined skeletal structure which produces high quality pose dependent deformations. Our framework learns to rig and skin characters with the same articulation structure (
e.g.
, bipeds or quadrupeds), and builds the desired skeleton hierarchy into the network architecture.
Furthermore
, we propose
neural blend shapes
- a set of corrective pose-dependent shapes which improve the deformation quality in the joint regions in order to address the notorious artifacts resulting from standard rigging and skinning. Our system estimates neural blend shapes for input meshes with arbitrary connectivity, as well as weighting coefficients which are conditioned on the input joint rotations. Unlike recent deep learning techniques which supervise the network with ground-truth rigging and skinning parameters, our approach does not assume that the training data has a specific underlying deformation model. Instead, during training, the network observes deformed shapes and learns to infer the corresponding rig, skin and blend shapes using
indirect supervision.
During inference, we demonstrate that our network generalizes to unseen characters with arbitrary mesh connectivity, including unrigged characters built by 3D artists. Conforming to standard skeletal animation models enables direct plug-and-play in standard animation software, as well as game engines.
A time-independent deformer for elastic contacts
Authors: Brunel Camille , Bénard Pierre , Guennebaud Gaël
Published: July 2021

We present a purely geometric, time-independent deformer resolving local contacts between elastic objects, including self-collisions between adjacent parts of the same object that often occur in character skinning animation. Starting from multiple meshes in intersection, our deformer first computes the parts of the surfaces remaining in contact, and then applies a procedural displacement with volume preservation. Although our deformer processes each frame independently, it achieves temporally continuous deformations with artistic control of the bulge through few pseudo-stiffness parameters. The plausibility of the deformation is further enhanced by anisotropically spreading the volume-preserving bulge. The result is a robust, real-time deformer that can handle complex geometric configurations such as a ball squashed by a hand, colliding lips, bending fingers, etc.
Neural animation layering for synthesizing martial arts movements
Authors: Starke Sebastian , Zhao Yiwei , Zinno Fabio , Komura Taku
Published: July 2021

Interactively synthesizing novel combinations and variations of character movements from different motion skills is a key problem in computer animation. In this paper, we propose a deep learning framework to produce a large variety of martial arts movements in a controllable manner from raw motion capture data. Our method imitates animation layering using neural networks with the aim to overcome typical challenges when mixing, blending and editing movements from unaligned motion sources. The framework can synthesize novel movements from given reference motions and simple user controls, and generate unseen sequences of locomotion, punching, kicking, avoiding and combinations thereof, but also reconstruct signature motions of different fighters, as well as close-character interactions such as clinching and carrying by learning the spatial joint relationships. To achieve this goal, we adopt a modular framework which is composed of the motion generator and a set of different control modules. The motion generator functions as a motion manifold that projects novel mixed/edited trajectories to natural full-body motions, and synthesizes realistic transitions between different motions. The control modules are task dependent and can be developed and trained separately by engineers to include novel motion tasks, which greatly reduces network iteration time when working with large-scale datasets. Our modular framework provides a transparent control interface for animators that allows modifying or combining movements after network training, and enables iterative adding of control modules for different motion tasks and behaviors. Our system can be used for offline and online motion generation alike, and is relevant for real-time applications such as computer games.
Learning an animatable detailed 3D face model from in-the-wild images
Authors: Feng Yao , Feng Haiwen , Black Michael J. , Bolkart Timo
Published: July 2021

While current monocular 3D face reconstruction methods can recover fine geometric details, they suffer several limitations. Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression. Other methods are trained on high-quality face scans and do not generalize well to in-the-wild images. We present the first approach that regresses 3D face shape and animatable details that are specific to an individual but change with expression. Our model, DECA (Detailed Expression Capture and Animation), is trained to robustly produce a UV displacement map from a low-dimensional latent representation that consists of person-specific detail parameters and generic expression parameters, while a regressor is trained to predict detail, shape, albedo, expression, pose and illumination parameters from a single image. To enable this, we introduce a novel detail-consistency loss that disentangles person-specific details from expression-dependent wrinkles. This disentanglement allows us to synthesize realistic person-specific wrinkles by controlling expression parameters while keeping person-specific details unchanged. DECA is learned from in-the-wild images with no paired 3D supervision and achieves state-of-the-art shape reconstruction accuracy on two benchmarks. Qualitative results on in-the-wild data demonstrate DECA's robustness and its ability to disentangle identity- and expression-dependent details enabling animation of reconstructed faces. The model and code are publicly available at https://deca.is.tue.mpg.de.
An unbiased ray-marching transmittance estimator
Authors: Kettunen Markus , D'Eon Eugene , Pantaleoni Jacopo , Novák Jan
Published: July 2021

We present an in-depth analysis of the sources of variance in state-of-the-art unbiased volumetric transmittance estimators, and propose several new methods for improving their efficiency. These combine to produce a single estimator that is universally optimal relative to prior work, with up to several orders of magnitude lower variance at the same cost, and has zero variance for any ray with non-varying extinction. We first reduce the variance of truncated power-series estimators using a novel efficient application of U-statistics. We then greatly reduce the average expansion order of the power series and redistribute density evaluations to filter the optical depth estimates with an equidistant sampling comb. Combined with the use of an online control variate built from a sampled mean density estimate, the resulting estimator effectively performs ray marching most of the time while using rarely-sampled higher-order terms to correct the bias.