bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Advances in Drug Delivery System for Bioactive Glycosides from Traditional Chinese Medicine
Authors: Li Lei , Feng Yi , Hong Yanlong , Lin Xiao , Shen Lan
Published: November 2018

Traditional Chinese Medicine (TCM) has been used in China for thousands of years for the prevention and treatment of various diseases. The materials that exert a therapeutic effect are called the active ingredients. The bioactive glycosides are important active ingredients from TCM that can make significant contributions to treating diseases. Because of the possibilities of various clinical applications, the properties and administration of these bioactive glycosides deserve further investigation. Their promising treatment effects, however, are hindered by their poor solubility, poor stability and rapid elimination. Therefore, it is necessary that we improve the therapeutic efficacy of bioactive glycosides by overcoming these problems. Meanwhile, some practical design strategies and novel drug delivery vehicles based on drug delivery systems provide favorable support in clinical practice for these active ingredients. This review summarizes diverse pharmacological activities of bioactive glycosides and focuses on recent advances in delivery system for these active constitutes; in particular, some glycol glycosides can effectively cure intractable diseases through targeted drug delivery. This review elucidates some design strategies for drug delivery system that are mainly based on two methods (avoiding physical barriers by changing dosage forms and enhancing the ability to bind to receptors or proteins after administration) and indicate the current challenges during the combination of delivery vehicles and these glycosides in hopes of promoting the process of receiving ideal therapeutic efficacy of them in future studies.
Searching for new physics in charm radiative decays
Authors: Biswas Aritra , Mandal Sanjoy , Sinha Nita
Published: November 2018

We show that for a heavy vector-like quark model with a down type isosinglet, branching ratio for [Formula: see text] decay is enhanced by more than [Formula: see text] as compared to that in the Standard model when QCD corrections to next-to-leading order are incorporated. In a left–right symmetric model (LRSM) along with a heavy vector-like fermion, enhancement of this order can be achieved at the bare (QCD uncorrected) level itself. We propose that a measurement of the photon polarization could be used to signal the presence of such new physics in spite of the large long distance effects. We find that there is a large region within the allowed parameter space of the model with a vector-like quark and an additional left–right symmetry, where, the photon polarization can be dominantly right-handed.
Representation of freshwater aquaculture fish behavior in low dissolved oxygen condition based on 3D computer vision
Authors: Bao Y. J. , Ji C. Y. , Zhang B. , Gu J. L.
Published: November 2018

Dissolved oxygen (DO) plays an important role in industrialized freshwater aquaculture. Such deficiencies such as the high cost of water-quality monitoring system and the failure to accurately monitor or describe aquaculture water-quality existed in freshwater aquaculture water-quality monitoring system. Here, a kind of representation method applied to characterize industrialized aquaculture fish behavior in different degrees of DO deficiency is based on three-dimensional (3D) Computer Vision. 3D coordinate values of aquaculture fishes in water acquired from 3D Computer Vision Device by processing aquaculture fish image are applied to represent such parameters as the average activity and height of aquaculture fish in water. This method for representing different behaviors of industrialized freshwater aquaculture fish under the condition of anoxia is realized by using these parameters and combing with the experience of aquaculture. The results show that the representation of industrialized freshwater aquaculture fish based on 3D Computer Vision System can be applied to describe industrialized aquaculture fish behavior and effectively compensate for the shortfall spatial location of aquaculture fish unable to acquire from 2D monitoring system, which is helpful for the accurate and reasonable control of DO in aquaculture.
Finite groups with a unique non-abelian proper subgroup
Authors: Taeri Bijan , Tayanloo-Beyg Fatemeh
Published: November 2018

In this paper we classify finite non-abelian groups having a unique non-abelian proper subgroup.
Directed Exploration in Black-Box Optimization for Multi-Objective Reinforcement Learning
Authors: García Javier , Iglesias Roberto , Rodríguez Miguel A. , Regueiro Carlos V.
Published: November 2018

Usually, real-world problems involve the optimization of multiple, possibly conflicting, objectives. These problems may be addressed by Multi-objective Reinforcement learning (MORL) techniques. MORL is a generalization of standard Reinforcement Learning (RL) where the single reward signal is extended to multiple signals, in particular, one for each objective. MORL is the process of learning policies that optimize multiple objectives simultaneously. In these problems, the use of directional/gradient information can be useful to guide the exploration to better and better behaviors. However, traditional policy-gradient approaches have two main drawbacks: they require the use of a batch of episodes to properly estimate the gradient information (reducing in this way the learning speed), and they use stochastic policies which could have a disastrous impact on the safety of the learning system. In this paper, we present a novel population-based MORL algorithm for problems in which the underlying objectives are reasonably smooth. It presents two main characteristics: fast computation of the gradient information for each objective through the use of neighboring solutions, and the use of this information to carry out a geometric partition of the search space and thus direct the exploration to promising areas. Finally, the algorithm is evaluated and compared to policy gradient MORL algorithms on different multi-objective problems: the water reservoir and the biped walking problem (the latter both on simulation and on a real robot).
Frame-related operators for woven frames
Authors: Rahimi A. , Samadzadeh Z. , Daraby B.
Published: November 2018

A new notion in frame theory has been introduced recently under the name woven-weaving frames by Bemrose et al. In the study of frames, some operators like analysis, synthesis, Gram and frame operators play the central role. In this paper, for the first time, we introduce and define these operators for woven-weaving frames and review some of their properties. In continuation, we investigate the effect of different types of operators on the woven frames and their bounds. Also, we provide some conditions that shows sum of woven frames are also woven frames. Finally, we apply these properties with an example.
Translation surfaces in the three-dimensional simply isotropic space 𝕀31
Authors: Karacan Murat Kemal , Yoon Dae Won , Bukcu Bahaddin
Published: November 2018

In [M. K. Karacan, D. W. Yoon and B. Bukcu, Translation surfaces in the three-dimensional simply isotropic space [Formula: see text], Int. J. Geom. Methods Mod. Phys. 13(7) (2016) 1650088], there is a mistake in Theorem 5 that appeared in the paper. We here provide a correct theorem.
On the moduli of a Dedekind sum
Authors: Girstmair Kurt
Published: November 2018

Let [Formula: see text] denote the classical Dedekind sum and [Formula: see text]. Let [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], be the value of [Formula: see text]. In a previous paper, we showed that there are pairs [Formula: see text], [Formula: see text], such that [Formula: see text] for all [Formula: see text], the [Formula: see text]’s growing in [Formula: see text] exponentially. Here we exhibit such a sequence with [Formula: see text] a polynomial of degree [Formula: see text] in [Formula: see text].
On values taken by the largest prime factor of shifted primes (II)
Authors: Chen Bin , Wu Jie
Published: November 2018

Denote by [Formula: see text] the set of all primes and by [Formula: see text] the largest prime factor of integer [Formula: see text] with the convention [Formula: see text]. Let [Formula: see text] be the unique positive solution of the equation [Formula: see text] in [Formula: see text]. Very recently Wu proved that for [Formula: see text] there is a constant [Formula: see text] such that for each fixed non-zero integer [Formula: see text] the set [Formula: see text] has relative density 1 in [Formula: see text]. In this paper, we shall further extend the domain of [Formula: see text] at the cost of obtaining a lower bound in place of an asymptotic formula, by showing that for each [Formula: see text] the set [Formula: see text] has relative positive density in [Formula: see text].
Effect of Initial Indentation Position on Plastic Deformation Behaviors of Polycrystalline Materials via Molecular Dynamics Simulation
Authors: Zhao Pengyue , Guo Yongbo
Published: November 2018

Polycrystalline materials can be divided into four types of microstructural components, including grain cell (GC), grain boundary (GB), triple junction (TJ) and vertex points (VP). Nanoindentation at different microstructural components on the polycrystalline materials surface can lead to different plastic deformation behaviors of the polycrystalline materials. Due to experimental limitations, the indentation-induced internal stress and defect evolution process are difficult to investigate directly, especially for the polycrystalline materials with grain size less than 100[Formula: see text]nm. The molecular dynamics (MD) simulations were performed to unravel the initial indentation position effect on the elasticity/plastic deformation mechanism of polycrystalline copper. The results reveal that the initial indentation position governs the indentation force variation and defect distribution range due to the different dimensionalities of the microstructural components. The defect propagation as well as the internal stress transmission in the GC regions tend to transfer to the low-dimensional microstructural components of the interfaces. In addition, the atomic internal stress and potential energy accumulation/release of the microstructural component atoms during the nanoindentation process are also investigated, revealing that the atomic internal stress and potential energy in the VPs vary earliest, followed by the TJs, GBs and GCs.
Existence results for some impulsive partial functional fractional differential equations
Authors: Hammouche Hadda , Lemkeddem Mouna , Guerbati Kaddour , Ezzinbi Khalil
Published: November 2018

In this paper, we study the existence of mild solutions of impulsive evolution fractional functional differential equation of order [Formula: see text] involving a Lipschitz condition on term [Formula: see text]. We shall rely on a fixed point theorem for the sum of completely continuous and contraction operators due to Burton and Kirk.
One-pot synthesis of potassium iron hexacyanoferrate/polyacrylamide nanohybrid hydrogel via gamma radiation and its adsorption property
Authors: Chang Shuquan , Liu Yangzhou , Fu Heliang , Wang Buxiong , Li Zheng , Feng Jundong , Zhang Xiaohong , Dai Yaodong , Zhang Haiqian
Published: November 2018

Potassium iron hexacyanoferrate/polyacrylamide nanohybrid hydrogel (PB/PAM-gel) was successfully synthesized via a convenient one-pot gamma radiation route. The as-prepared PB/PAM-gel has compact internal three-dimensional porous structures with well-dispersed PB nanowires, which exhibit good elasticity and excellent swelling performance. The adsorption experiments reveal that the PB/PAM-gel can rapidly and selectively adsorb Cs[Formula: see text] from the solution in a wide range of pH. The maximum adsorption capacity reaches 131[Formula: see text]mg[Formula: see text]g[Formula: see text]. This nanohybrid hydrogel is very stable and can be easily separated from the solution after adsorption. It can be applied as a potential adsorbent for Cs[Formula: see text] removal from radioactive wastewater.
On the genus of the k-maximal hypergraph of commutative rings
Authors: Selvakumar K. , Amritha V. C.
Published: November 2018

Let [Formula: see text] be a commutative ring with identity and [Formula: see text], a fixed integer. Let [Formula: see text] be the set of all [Formula: see text]-maximal elements in [Formula: see text] Associate a [Formula: see text]-maximal hypergraph [Formula: see text] to [Formula: see text] with vertex set [Formula: see text] and for distinct elements [Formula: see text] in [Formula: see text], the set [Formula: see text] is an edge of [Formula: see text] if and only if [Formula: see text] and [Formula: see text] for all [Formula: see text]. In this paper, we determine all isomorphism classes of finite commutative non-local rings with identity whose [Formula: see text]-maximal hypergraph has genus one. Finally, we classify all finite commutative non-local rings [Formula: see text] for which [Formula: see text] is projective.
Towards the multi-scale characterization of braided fluvial geobodies from outcrop, core, ground-penetrating radar and well log data
Authors: Yeste Luis Miguel , Henares Saturnina , McDougall Neil , García-García Fernando , Viseras César
Published: November 2018

The integrated application of advanced visualization techniques – validated against outcrop, core and gamma ray log data – was found to be crucial in characterizing the spatial distribution of fluvial facies and their inherent permeability baffles to a centimetre-scale vertical resolution. An outcrop/behind outcrop workflow was used, combining the sedimentological analysis of a perennial deep braided outcrop with ground-penetrating radar profiles, behind outcrop optical and acoustic borehole imaging, and the analyses of dip tadpoles, core and gamma ray logs. Data from both the surface and subsurface allowed the recognition of two main architectural elements – channels and compound bars – and within the latter to distinguish between the bar head and tail and the cross-bar channel. On the basis of a well-constrained sedimentological framework, a detailed characterization of the gamma ray log pattern in the compound bar allowed several differences between the architectural elements to be identified, despite a general cylindrical trend. A high-resolution tadpole analysis showed that a random pattern prevailed in the channel, whereas in the bar head and tail the tadpoles displayed characteristic patterns that allowed differentiation. The ground-penetrating radar profiles aided the 3D reconstruction of each architectural element. Thus the application of this outcrop/behind outcrop workflow provided a solid database for the characterization of reservoir rock properties from outcrop analogues.
UCR
Authors: Yang Kun , Botero Ulbert , Shen Haoting , Woodard Damon L. , Forte Domenic , Tehranipoor Mark M.
Published: November 2018

Chipless Radio Frequency Identification (RFID) tags that do not include an integrated circuit (IC) in the transponder are more appropriate for supply-chain management of low-cost commodities and have been gaining extensive attention due to their relatively lower price. However, existing chipless RFID tags consume considerable tag area and manufacturing time/cost because of complex fabrication process (e.g., requiring removing or shorting some resonators on the tag substrate to encode data). Worse still, their identifiers (IDs) are deterministic, clonable, and small in terms of bitwidth. To address these shortcomings and help preserve the cold chain for commodities (e.g., vaccines, pharmaceuticals, etc.) sensitive to temperature, we develop a novel unclonable environmentally sensitive chipless RFID (UCR) tag that intrinsically generates a unique ID from both manufacturing variations and ambient temperature variation. A UCR tag consists of two parts: (i) a certain number of concentric ring slot resonators integrated on a certain laminate (e.g., TACONIC TLX-0), whose resonance frequencies rely on geometric parameters of slot resonators and dielectric constant of substrate material that are sensitive to manufacturing variations, and (ii) a stand-alone circular ring slot resonator integrated on a particular substrate (e.g., grease) that will be melted at a high temperature, whose resonance frequency relies on geometric parameters of slot resonator, dielectric constant of substrate material, and ambient temperature. UCR tags have the capability to track commodities and their temperatures in the supply chain. The area of UCR tag is comparable to regular quick response (QR) code. Experimental results based on UCR tag prototypes have verified their uniqueness and reliability.
Simultaneous acquisition of polarimetric SVBRDF and normals
Authors: Baek Seung-Hwan , Jeon Daniel S. , Tong Xin , Kim Min H.
Published: November 2018

Capturing appearance often requires dense sampling in light-view space, which is often achieved in specialized, expensive hardware setups. With the aim of realizing a compact acquisition setup without multiple angular samples of light and view, we sought to leverage an alternative optical property of light,
polarization.
To this end, we capture a set of polarimetric images with linear polarizers in front of a single projector and camera to obtain the appearance and normals of real-world objects. We encountered two technical challenges: First, no complete polarimetric BRDF model is available for modeling mixed polarization of both specular and diffuse reflection. Second, existing polarization-based inverse rendering methods are not applicable to a single local illumination setup since they are formulated with the assumption of spherical illumination. To this end, we first present a complete polarimetric BRDF (pBRDF) model that can define mixed polarization of both specular and diffuse reflection. Second, by leveraging our pBRDF model, we propose a novel inverse-rendering method with joint optimization of pBRDF and normals to capture spatially-varying material appearance: per-material specular properties (including the refractive index, specular roughness and specular coefficient), per-pixel diffuse albedo and normals. Our method can solve the severely ill-posed inverse-rendering problem by carefully accounting for the physical relationship between polarimetric appearance and geometric properties. We demonstrate how our method overcomes limited sampling in light-view space for inverse rendering by means of polarization.
Inexact descent methods for elastic parameter optimization
Authors: Yan Guowei , Li Wei , Yang Ruigang , Wang Huamin
Published: November 2018

Elastic parameter optimization has revealed its importance in 3D modeling, virtual reality, and additive manufacturing in recent years. Unfortunately, it is known to be computationally expensive, especially if there are many parameters and data samples. To address this challenge, we propose to introduce the inexactness into descent methods, by iteratively solving a forward simulation step and a parameter update step in an inexact manner. The development of such inexact descent methods is centered at two questions: 1) how accurate/inaccurate can the two steps be; and 2) what is the optimal way to implement an inexact descent method. The answers to these questions are in our convergence analysis, which proves the existence of relative error thresholds for the two inexact steps to ensure the convergence. This means we can simply solve each step by a fixed number of iterations, if the iterative solver is at least linearly convergent. While the use of the inexact idea speeds up many descent methods, we specifically favor a GPU-based one powered by state-of-the-art simulation techniques. Based on this method, we study a variety of implementation issues, including backtracking line search, initialization, regularization, and multiple data samples. We demonstrate the use of our inexact method in elasticity measurement and design applications. Our experiment shows the method is fast, reliable, memory-efficient, GPU-friendly, flexible with different elastic models, scalable to a large parameter space, and parallelizable for multiple data samples.
Automatic unpaired shape deformation transfer
Authors: Gao Lin , Yang Jie , Qiao Yi-Ling , Lai Yu-Kun , Rosin Paul L. , Xu Weiwei , Xia Shihong
Published: November 2018

Transferring deformation from a source shape to a target shape is a very useful technique in computer graphics. State-of-the-art deformation transfer methods require either point-wise correspondences between source and target shapes, or pairs of deformed source and target shapes with corresponding deformations. However, in most cases, such correspondences are not available and cannot be reliably established using an automatic algorithm. Therefore, substantial user effort is needed to label the correspondences or to obtain and specify such shape sets. In this work, we propose a novel approach to automatic deformation transfer between two unpaired shape sets without correspondences. 3D deformation is represented in a high-dimensional space. To obtain a more compact and effective representation, two convolutional variational autoencoders are learned to encode source and target shapes to their latent spaces. We exploit a Generative Adversarial Network (GAN) to map deformed source shapes to deformed target shapes, both in the latent spaces, which ensures the obtained shapes from the mapping are indistinguishable from the target shapes. This is still an under-constrained problem, so we further utilize a reverse mapping from target shapes to source shapes and incorporate cycle consistency loss, i.e. applying both mappings should reverse to the input shape. This VAE-Cycle GAN (VC-GAN) architecture is used to build a reliable mapping between shape spaces. Finally, a similarity constraint is employed to ensure the mapping is consistent with visual similarity, achieved by learning a similarity neural network that takes the embedding vectors from the source and target latent spaces and predicts the light field distance between the corresponding shapes. Experimental results show that our fully automatic method is able to obtain high-quality deformation transfer results with unpaired data sets, comparable or better than existing methods where strict correspondences are required.
DeepFocus
Authors: Xiao Lei , Kaplanyan Anton , Fix Alexander , Chapman Matthew , Lanman Douglas
Published: November 2018

Addressing vergence-accommodation conflict in head-mounted displays (HMDs) requires resolving two interrelated problems. First, the hardware must support viewing sharp imagery over the full accommodation range of the user. Second, HMDs should accurately reproduce retinal defocus blur to correctly drive accommodation. A multitude of
accommodation-supporting
HMDs have been proposed, with three architectures receiving particular attention: varifocal, multifocal, and light field displays. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate defocus blur (often limiting content complexity and interactive applications). To date, no unified framework has been proposed to support driving these emerging HMDs using commodity content. In this paper, we introduce
DeepFocus
, a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.
3D fabrication with universal building blocks and pyramidal shells
Authors: Chen Xuelin , Li Honghua , Fu Chi-Wing , Zhang Hao , Cohen-Or Daniel , Chen Baoquan
Published: November 2018

We introduce a computational solution for cost-efficient 3D fabrication using
universal building blocks.
Our key idea is to employ a set of universal blocks, which can be massively prefabricated at a low cost, to quickly assemble and constitute a significant
internal core
of the target object, so that only the
residual volume
need to be 3D printed online. We further improve the fabrication efficiency by decomposing the residual volume into a small number of printing-friendly pyramidal pieces. Computationally, we face a
coupled decomposition
problem: decomposing the input object into an internal core and residual, and decomposing the residual, to fulfill a combination of objectives for efficient 3D fabrication. To this end, we formulate an optimization that jointly minimizes the residual volume, the number of pyramidal residual pieces, and the amount of support waste when printing the residual pieces. To solve the optimization in a tractable manner, we start with a maximal internal core and iteratively refine it with local cuts to minimize the cost function. Moreover, to efficiently explore the large search space, we resort to cost estimates aided by pre-computation and avoid the need to explicitly construct pyramidal decompositions for each solution candidate. Results show that our method can iteratively reduce the estimated printing time and cost, as well as the support waste, and helps to save hours of fabrication time and much material consumption.
Language-driven synthesis of 3D scenes from scene databases
Authors: Ma Rui , Patil Akshay Gadi , Fisher Matthew , Li Manyi , Pirk Sören , Hua Binh-Son , Yeung Sai-Kit , Tong Xin , Guibas Leonidas , Zhang Hao
Published: November 2018

We introduce a novel framework for using natural language to generate and edit 3D indoor scenes, harnessing scene semantics and text-scene grounding knowledge learned from large annotated 3D scene databases. The advantage of natural language editing interfaces is strongest when performing semantic operations at the
sub-scene
level, acting on
groups
of objects. We learn how to manipulate these sub-scenes by analyzing existing 3D scenes. We perform edits by first parsing a natural language command from the user and transforming it into a
semantic scene graph
that is used to retrieve corresponding sub-scenes from the databases that match the command. We then augment this retrieved sub-scene by incorporating other objects that may be implied by the scene context. Finally, a new 3D scene is synthesized by aligning the augmented sub-scene with the user's current scene, where new objects are spliced into the environment, possibly triggering appropriate adjustments to the existing scene arrangement. A suggestive modeling interface with multiple interpretations of user commands is used to alleviate ambiguities in natural language. We conduct studies comparing our approach against both prior text-to-scene work and artist-made scenes and find that our method significantly outperforms prior work and is comparable to handmade scenes even when complex and varied natural sentences are used.
Inverse elastic shell design with contact and friction
Authors: Ly Mickaël , Casati Romain , Bertails-Descoubes Florence , Skouras Mélina , Boissieux Laurence
Published: November 2018

We propose an inverse strategy for modeling thin elastic shells physically, just from the observation of their geometry. Our algorithm takes as input an arbitrary target mesh, and interprets this configuration automatically as a stable equilibrium of a shell simulator under gravity and frictional contact constraints with a given external object. Unknowns are the natural shape of the shell (i.e., its shape without external forces) and the frictional contact forces at play, while the material properties (mass density, stiffness, friction coefficients) can be freely chosen by the user. Such an inverse problem formulates as an ill-posed nonlinear system subject to conical constraints. To select and compute a plausible solution, our inverse solver proceeds in two steps. In a first step, contacts are reduced to frictionless bilateral constraints and a natural shape is retrieved using the adjoint method. The second step uses this result as an initial guess and adjusts each bilateral force so that it projects onto the admissible Coulomb friction cone, while preserving global equilibrium. To better guide minimization towards the target, these two steps are applied iteratively using a degressive regularization of the shell energy.

We validate our approach on simulated examples with reference material parameters, and show that our method still converges well for material parameters lying within a reasonable range around the reference, and even in the case of arbitrary meshes that are not issued from a simulation. We finally demonstrate practical inversion results on complex shell geometries freely modeled by an artist or automatically captured from real objects, such as posed garments or soft accessories.
Deep motifs and motion signatures
Authors: Aristidou Andreas , Cohen-Or Daniel , Hodgins Jessica K. , Chrysanthou Yiorgos , Shamir Ariel
Published: November 2018

Many analysis tasks for human motion rely on high-level similarity between sequences of motions, that are not an exact matches in joint angles, timing, or ordering of actions. Even the same movements performed by the same person can vary in duration and speed. Similar motions are characterized by similar sets of actions that appear frequently. In this paper we introduce
motion motifs
and
motion signatures
that are a succinct but descriptive representation of motion sequences. We first break the motion sequences to short-term movements called motion words, and then cluster the words in a high-dimensional feature space to find motifs. Hence, motifs are words that are both common and descriptive, and their distribution represents the motion sequence. To cluster words and find motifs, the challenge is to define an effective feature space, where the distances among motion words are semantically meaningful, and where variations in speed and duration are handled. To this end, we use a deep neural network to embed the motion words into feature space using a triplet loss function. To define a signature, we choose a finite set of motion-motifs, creating a bag-of-motifs representation for the sequence. Motion signatures are agnostic to movement order, speed or duration variations, and can distinguish fine-grained differences between motions of the same class. We illustrate examples of characterizing motion sequences by motifs, and for the use of motion signatures in a number of applications.
Modeling hair from an RGB-D camera
Authors: Zhang Meng , Wu Pan , Wu Hongzhi , Weng Yanlin , Zheng Youyi , Zhou Kun
Published: November 2018

Creating realistic 3D hairs that closely match the real-world inputs remains challenging. With the increasing popularity of lightweight depth cameras featured in devices such as iPhone X, Intel RealSense and DJI drones, depth cues can be very helpful in consumer applications, for example, the Animated Emoji. In this paper, we introduce a fully automatic, data-driven approach to model the hair geometry and compute a complete strand-level 3D hair model that closely resembles the input from a single RGB-D camera. Our method heavily exploits the geometric cues contained in the depth channel and leverages exemplars in a 3D hair database for high-fidelity hair synthesis. The core of our method is a local-similarity based search and synthesis algorithm that simultaneously reasons about the hair geometry, strands connectivity, strand orientation, and hair structural plausibility. We demonstrate the efficacy of our method using a variety of complex hairstyles and compare our method with prior arts.
OptCuts
Authors: Li Minchen , Kaufman Danny M. , Kim Vladimir G. , Solomon Justin , Sheffer Alla
Published: November 2018

Low-distortion mapping of three-dimensional surfaces to the plane is a critical problem in geometry processing. The intrinsic distortion introduced by these UV mappings is highly dependent on the choice of surface cuts that form seamlines which break mapping continuity. Parameterization applications typically require UV maps with an application-specific upper bound on distortion to avoid mapping artifacts; at the same time they seek to reduce cut lengths to minimize discontinuity artifacts. We propose
OptCuts
, an algorithm that jointly optimizes the parameterization and cutting of a three-dimensional mesh. OptCuts starts from an arbitrary initial embedding and a user-requested distortion bound. It requires no parameter setting and automatically seeks to minimize seam lengths subject to satisfying the distortion bound of the mapping computed using these seams. OptCuts alternates between topology and geometry update steps that consistently decrease distortion and seam length, producing a UV map with compact boundaries that strictly satisfies the distortion bound. OptCuts automatically produces high-quality, globally bijective UV maps without user intervention. While OptCuts can thus be a highly effective tool to create new mappings from scratch, we also show how it can be employed to improve pre-existing embeddings. Additionally, when semantic or other priors on seam placement are desired, OptCuts can be extended to respect these user preferences as constraints during optimization of the parameterization. We demonstrate the scalable performance of OptCuts on a wide range of challenging benchmark parameterization examples, as well as in comparisons with state-of-the-art UV methods and commercial tools.
GPU optimization of material point methods
Authors: Gao Ming , Wang Xinlei , Wu Kui , Pradhana Andre , Sifakis Eftychios , Yuksel Cem , Jiang Chenfanfu
Published: November 2018

The Material Point Method (MPM) has been shown to facilitate effective simulations of physically complex and topologically challenging materials, with a wealth of emerging applications in computational engineering and visual computing. Borne out of the extreme importance of regularity, MPM is given attractive parallelization opportunities on high-performance modern multiprocessors. Parallelization of MPM that fully leverages computing resources presents challenges that require exploring an extensive design-space for favorable data structures and algorithms. Unlike the conceptually simple CPU parallelization, where the coarse partition of tasks can be easily applied, it takes greater effort to reach the GPU hardware saturation due to its many-core SIMT architecture. In this paper we introduce methods for addressing the computational challenges of MPM and extending the capabilities of general simulation systems based on MPM, particularly concentrating on GPU optimization. In addition to our open-source high-performance framework, we also conduct performance analyses and benchmark experiments to compare against alternative design choices which may superficially appear to be reasonable, but can suffer from suboptimal performance in practice. Our explicit and fully implicit GPU MPM solvers are further equipped with a Moving Least Squares MPM heat solver and a novel sand constitutive model to enable fast simulations of a wide range of materials. We demonstrate that more than an order of magnitude performance improvement can be achieved with our GPU solvers. Practical high-resolution examples with up to ten million particles run in less than one minute per frame.
Position-free monte carlo simulation for arbitrary layered BSDFs
Authors: Guo Yu , Hašan Miloš , Zhao Shuang
Published: November 2018

Real-world materials are often layered: metallic paints, biological tissues, and many more. Variation in the interface and volumetric scattering properties of the layers leads to a rich diversity of material appearances from anisotropic highlights to complex textures and relief patterns. However, simulating light-layer interactions is a challenging problem. Past analytical or numerical solutions either introduce several approximations and limitations, or rely on expensive operations on discretized BSDFs, preventing the ability to freely vary the layer properties spatially. We introduce a new unbiased layered BSDF model based on Monte Carlo simulation, whose only assumption is the layer assumption itself. Our novel position-free path formulation is fundamentally more powerful at constructing light transport paths than generic light transport algorithms applied to the special case of flat layers, since it is based on a product of solid angle instead of area measures, so does not contain the high-variance geometry terms needed in the standard formulation. We introduce two techniques for sampling the position-free path integral, a forward path tracer with next-event estimation and a full bidirectional estimator. We show a number of examples, featuring multiple layers with surface and volumetric scattering, surface and phase function anisotropy, and spatial variation in all parameters.
Learning to reconstruct shape and spatially-varying reflectance from a single image
Authors: Li Zhengqin , Xu Zexiang , Ramamoorthi Ravi , Sunkavalli Kalyan , Chandraker Manmohan
Published: November 2018

Reconstructing shape and reflectance properties from images is a highly under-constrained problem, and has previously been addressed by using specialized hardware to capture calibrated data or by assuming known (or highly constrained) shape or reflectance. In contrast, we demonstrate that we can recover non-Lambertian, spatially-varying BRDFs and complex geometry belonging to any arbitrary shape class, from a single RGB image captured under a combination of unknown environment illumination and flash lighting. We achieve this by training a deep neural network to regress shape and reflectance from the image. Our network is able to address this problem because of three novel contributions: first, we build a large-scale dataset of procedurally generated shapes and real-world complex SVBRDFs that approximate real world appearance well. Second, single image inverse rendering requires reasoning at multiple scales, and we propose a cascade network structure that allows this in a tractable manner. Finally, we incorporate an in-network rendering layer that aids the reconstruction task by handling global illumination effects that are important for real-world scenes. Together, these contributions allow us to tackle the entire inverse rendering problem in a holistic manner and produce state-of-the-art results on both synthetic and real data.
Deep multispectral painting reproduction via multi-layer, custom-ink printing
Authors: Shi Liang , Babaei Vahid , Kim Changil , Foshey Michael , Hu Yuanming , Sitthi-Amorn Pitchaya , Rusinkiewicz Szymon , Matusik Wojciech
Published: November 2018

We propose a workflow for spectral reproduction of paintings, which captures a painting's spectral color, invariant to illumination, and reproduces it using multi-material 3D printing. We take advantage of the current 3D printers' capabilities of combining highly concentrated inks with a large number of layers, to expand the spectral gamut of a set of inks. We use a data-driven method to both predict the spectrum of a printed ink stack and optimize for the stack layout that best matches a target spectrum. This bidirectional mapping is modeled using a pair of neural networks, which are optimized through a problem-specific multi-objective loss function. Our loss function helps find the best possible ink layout resulting in the balance between spectral reproduction and colorimetric accuracy under a multitude of illuminants. In addition, we introduce a novel spectral vector error diffusion algorithm based on combining color contoning and halftoning, which simultaneously solves the layout discretization and color quantization problems, accurately and efficiently. Our workflow outperforms the state-of-the-art models for spectral prediction and layout optimization. We demonstrate reproduction of a number of real paintings and historically important pigments using our prototype implementation that uses 10 custom inks with varying spectra and a resin-based 3D printer.
An adaptive parameterization for efficient material acquisition and rendering
Authors: Dupuy Jonathan , Jakob Wenzel
Published: November 2018

One of the key ingredients of any physically based rendering system is a detailed specification characterizing the interaction of light and matter of all materials present in a scene, typically via the Bidirectional Reflectance Distribution Function (BRDF). Despite their utility, access to real-world BRDF datasets remains limited: this is because measurements involve scanning a four-dimensional domain at sufficient resolution, a tedious and often infeasibly time-consuming process.

We propose a new parameterization that automatically adapts to the behavior of a material, warping the underlying 4D domain so that most of the volume maps to regions where the BRDF takes on non-negligible values, while irrelevant regions are strongly compressed. This adaptation only requires a brief 1D or 2D measurement of the material's retro-reflective properties. Our parameterization is unified in the sense that it combines several steps that previously required intermediate data conversions: the same mapping can simultaneously be used for BRDF acquisition, storage, and it supports efficient Monte Carlo sample generation.

We observe that the above desiderata are satisfied by a core operation present in modern rendering systems, which maps uniform variates to direction samples that are proportional to an analytic BRDF. Based on this insight, we define our adaptive parameterization as an invertible, retro-reflectively driven mapping between the parametric and directional domains. We are able to create noise-free renderings of existing BRDF datasets after conversion into our representation with the added benefit that the warped data is significantly more compact, requiring 16KiB and 544KiB per spectral channel for isotropic and anisotropic specimens, respectively.

Finally, we show how to modify an existing gonio-photometer to provide the needed retro-reflection measurements. Acquisition then proceeds within a 4D space that is warped by our parameterization. We demonstrate the efficacy of this scheme by acquiring the first set of spectral BRDFs of surfaces exhibiting arbitrary roughness, including anisotropy.
Dynamical optimal transport on discrete surfaces
Authors: Lavenant Hugo , Claici Sebastian , Chien Edward , Solomon Justin
Published: November 2018

We propose a technique for interpolating between probability distributions on discrete surfaces, based on the theory of optimal transport. Unlike previous attempts that use linear programming, our method is based on a dynamical formulation of quadratic optimal transport proposed for flat domains by Benamou and Brenier [2000], adapted to discrete surfaces. Our structure-preserving construction yields a Riemannian metric on the (finite-dimensional) space of probability distributions on a discrete surface, which translates the so-called Otto calculus to discrete language. From a practical perspective, our technique provides a smooth interpolation between distributions on discrete surfaces with less diffusion than state-of-the-art algorithms involving entropic regularization. Beyond interpolation, we show how our discrete notion of optimal transport extends to other tasks, such as distribution-valued Dirichlet problems and time integration of gradient flows.
PhotoShape
Authors: Park Keunhong , Rematas Konstantinos , Farhadi Ali , Seitz Steven M.
Published: November 2018

Existing online 3D shape repositories contain thousands of 3D models but lack photorealistic appearance. We present an approach to automatically assign high-quality, realistic appearance models to large scale 3D shape collections. The key idea is to jointly leverage three types of online data - shape collections, material collections, and photo collections, using the photos as reference to guide assignment of materials to shapes. By generating a large number of synthetic renderings, we train a convolutional neural network to classify materials in real photos, and employ 3D-2D alignment techniques to transfer materials to different parts of each shape model. Our system produces photorealistic, relightable, 3D shapes (PhotoShapes).
Decoupling simulation accuracy from mesh quality
Authors: Schneider Teseo , Hu Yixin , Dumas Jérémie , Gao Xifeng , Panozzo Daniele , Zorin Denis
Published: November 2018

For a given PDE problem, three main factors affect the accuracy of FEM solutions: basis order, mesh resolution, and mesh element quality. The first two factors are easy to control, while controlling element shape quality is a challenge, with fundamental limitations on what can be achieved.

We propose to use
p
-refinement (increasing element degree) to decouple the approximation error of the finite element method from the domain mesh quality for elliptic PDEs.

Our technique produces an accurate solution even on meshes with badly shaped elements, with a slightly higher running time due to the higher cost of high-order elements. We demonstrate that it is able to automatically adapt the basis to badly shaped elements, ensuring an error consistent with high-quality meshing, without any per-mesh parameter tuning. Our construction reduces to traditional fixed-degree FEM methods on high-quality meshes with identical performance.

Our construction decreases the burden on meshing algorithms, reducing the need for often expensive mesh optimization and automatically compensates for badly shaped elements, which are present due to boundary constraints or limitations of current meshing methods. By tackling mesh generation and finite element simulation jointly, we obtain a pipeline that is both more efficient and more robust than combinations of existing state of the art meshing and FEM algorithms.
Delaunay mesh simplification with differential evolution
Authors: Yi Ran , Liu Yong-Jin , He Ying
Published: November 2018

Delaunay meshes (DM) are a special type of manifold triangle meshes --- where the local Delaunay condition holds everywhere --- and find important applications in digital geometry processing. This paper addresses the general DM simplification problem: given an arbitrary manifold triangle mesh
M
with
n
vertices and the user-specified resolution
m
(<
n
), compute a Delaunay mesh
M
*
with
m
vertices that has the least Hausdorffdistance to
M.
To solve the problem, we abstract the simplification process using a 2D Cartesian grid model, in which each grid point corresponds to triangle meshes with a certain number of vertices and a simplification process is a monotonic path on the grid. We develop a novel differential-evolution-based method to compute a low-cost path, which leads to a high quality Delaunay mesh. Extensive evaluation shows that our method consistently outperforms the existing methods in terms of approximation error. In particular, our method is highly effective for small-scale CAD models and man-made objects with sharp features but less details. Moreover, our method is fully automatic and can preserve sharp features well and deal with models with multiple components, whereas the existing methods often fail.
Interactive character animation by learning multi-objective control
Authors: Lee Kyungho , Lee Seyoung , Lee Jehee
Published: November 2018

We present an approach that learns to act from raw motion data for interactive character animation. Our motion generator takes a continuous stream of control inputs and generates the character's motion in an online manner. The key insight is modeling rich connections between a multitude of control objectives and a large repertoire of actions. The model is trained using Recurrent Neural Network conditioned to deal with spatiotemporal constraints and structural variabilities in human motion. We also present a new data augmentation method that allows the model to be learned even from a small to moderate amount of training data. The learning process is fully automatic if it learns the motion of a single character, and requires minimal user intervention if it deals with props and interaction between multiple characters.