bluuebunny/crossref_metadata_2025 huggingface dataset:
Resistive Switching in CdTe/CdSe Core–Shell Quantum Dots Embedded Chitosan-Based Memory Devices
Authors: Dlamini Zolile Wiseman , Vallabhapurapu Sreedevi , Daramola Olamide Abiodun , Tseki Potlaki Foster , Krause Rui Werner Macedo , Siwe-Noundou Xavier , Mahule Tebogo Sfiso , Vallabhapurapu Srinivasu Vijaya
Published: December 2021

In this paper, we report on the resistive switching (RS) and conduction mechanisms in devices consisting of CdTe/CdSe core–shell quantum dots embedded chitosan composites active layer. Two devices with active layers sandwiched between (1) Al and Ag, and (2) ITO and Ag electrodes were studied. Both devices exhibited bipolar memory behavior with [Formula: see text] V and [Formula: see text][Formula: see text]V, for the Al-based device, while [Formula: see text] V and [Formula: see text][Formula: see text]V were observed for the ITO-based device, enabling both devices to be operated at low powers. However, the switching mechanisms of both devices were different, i.e., RS in Al device was attributed to conductive bridge mechanism, while space-charge-limited driven conduction filament attributed the switching mechanism of the ITO device. Additionally, the Al-based device showed long retention ([Formula: see text][Formula: see text]s) and a reasonable large ([Formula: see text]) ON/OFF ratio. Additionally, for this device, we also observed sweeping cycle-induced reversal of voltage polarity of the [Formula: see text] and [Formula: see text]. In contrast, we observed that increasing sweeping cycles resulted in an exponential decrease of the OFF-state resistance of the ITO-based device.
Dynamical analysis for cylindrical geometry in non-minimally coupled f(R,T) gravity
Authors: Bhatti M. Z. , Yousaf Z. , Yousaf M.
Published: December 2021

This paper aims to investigate the stability constraints under the influence of particular modified gravity theory [Formula: see text], i.e. [Formula: see text] gravity in which the Lagrangian is a varying function of [Formula: see text] and trace of energy momentum tensor ([Formula: see text]). We examine stable behavior for compact cylindrical star having anisotropic symmetric configuration. We establish dynamical equations as well as equations of continuity in the background of this particular non-minimal coupled [Formula: see text]. We utilize perturbation technique which will be applied on geometrical as well as material physical quantities to constitute collapse equation. We continue this significant investigation to understand the dynamical behavior of considered cylindrical system under non-minimal coupled [Formula: see text] functional, i.e. [Formula: see text]. This gravitational function gives compatible findings only for [Formula: see text], also [Formula: see text] and [Formula: see text] considered in this astrophysical model as coupling entity. This model contains [Formula: see text] which is constant entity, having the values of order of the effective Ricci scalar [Formula: see text]. Furthermore, we impose some physical constraints to determine and maintain the stability criteria by establishing the expression of adiabatic index, i.e. [Formula: see text] for cylindrical anisotropic configuration, in Newtonian [Formula: see text] and post-Newtonian ([Formula: see text]) eras.
A study of anisotropic compact stars in f(R,ϕ,X) theory of gravity
Authors: Malik Adnan , Ahmad Iftikhar , Kiran
Published: December 2021

In this paper, we investigate the behavior of anisotropic compact stars in generalized modified gravity, namely [Formula: see text] gravity, where [Formula: see text] represents the Ricci scalar, [Formula: see text] is the scalar potential function and [Formula: see text] is a kinetic term of [Formula: see text]. We consider the spherically symmetric spacetime to analyze the feasible exposure of compact stars. We observe the behavior of anisotropic compact stars which includes Her X1, SAX J 1808.4-3658 and 4U 1820-30. From the graphical evaluation of energy density, tangential pressure, radial pressure, equilibrium conditions, energy conditions, mass–radius relationship, compactness and stability analysis of compact stars, it is concluded that the behavior of candidates of compact stars is regular in [Formula: see text] gravity for the considered parameter.
A sensitivity analysis of a single extraction well from deep geothermal aquifers in the Cheshire Basin, UK
Authors: Brown Christopher Simon , Cassidy Nigel J. , Egan Stuart S. , Griffiths Dan
Published: December 2021

Deep hot sedimentary aquifers (HSAs) are targeted for geothermal exploitation in the Cheshire Basin, UK. In this study, a single extraction well targeting the Collyhurst Sandstone Formation was modelled on MATLAB coupling heat and fluid flux. The Collyhurst Sandstone Formation in the Crewe area of the Cheshire Basin is expected to be found at a depth of 2.8–3.5 km, and was chosen as an area for geothermal exploration due to the high demand for energy.

Model results suggest that low-enthalpy, deep geothermal systems with thick HSAs are affected by both geological and engineering parameters. The results of this study highlight that the thermal gradient, hydraulic conductivity, production rate, length and position of the well screen are the key parameters capable of affecting the success and viability of any single well scheme. Poor planning during exploration and development can hinder the productivity of any single-well scheme, and these parameters must be considered to fully understand the risk. Engineering parameters, such as the length of the well screen, can be used during well planning to mitigate geological risks in the aquifer, whilst the results presented can also be used as a guide for energy potential under varying conditions.
Large- and medium-scale morphosedimentary features of the Messina Strait: insights into bottom-current-controlled sedimentation and interaction with downslope processes
Authors: Martorelli E. , Casalbore D. , Falcini F. , Bosman A. , Falese F. G. , Chiocci F. L.
Published: December 2021

The Messina Strait is a
c.
3–8 km-wide and 40 km-long extensional area that connects the Tyrrhenian Sea with the Ionian Sea (Mediterranean Sea), and where tectonics, oceanographic and erosive-depositional downslope processes strongly interact with each other. Based on the analysis of high-resolution multibeam data, we present an updated morphosedimentary framework that reveals a complex seabed morphology, characterized by a variety of features linked to bottom currents and downslope processes. In particular, we recognize a suite of large- to medium-scale erosive and depositional features, related to different bottom currents (e.g. reverse tidal flows, residual flows, internal waves) acting over diverse time periods. Large-scale bottom-current features are represented by contourite drifts and channels developed over long periods (more than thousands of years). Medium-scale features formed during shorter time periods, and include scours, furrows, transverse ridges (pinnacles) and narrow longitudinal bodies in the sill sector, along with several sand-wave fields, located at greater depths on the Ionian and Tyrrhenian sides of the Messina Strait. Downslope processes encompass channelized features originated by sedimentary gravity flows, coarse-grained aprons and fans, and submarine landslides. They mostly occur along the margins of the strait and become predominant in the southern exit where the axial Messina canyon and its tributaries are present.

Overall, our study shows that the Messina Strait is a fruitful area in which to investigate the interaction between recent erosive-depositional sedimentary and oceanographic processes, also modulated by sea-level fluctuations, during the last eustatic cycle. Moreover, the observed seabed morphologies and the associated processes provide insights into interpreting similar features in modern and ancient similar straits and seaways.
Synthesizing scene-aware virtual reality teleport graphs
Authors: Li Changyang , Huang Haikun , Lien Jyh-Ming , Yu Lap-Fai
Published: December 2021

We present a novel approach for synthesizing scene-aware virtual reality teleport graphs, which facilitate navigation in indoor virtual environments by suggesting desirable teleport positions. Our approach analyzes panoramic views at candidate teleport positions by extracting scene perception graphs, which encode scene perception relationships between the observer and the surrounding objects, and predict how desirable the views at these positions are. We train a graph convolutional model to predict the scene perception scores of different teleport positions. Based on such predictions, we apply an optimization approach to sample a set of desirable teleport positions while considering other navigation properties such as coverage and connectivity to synthesize a teleport graph. Using teleport graphs, users can navigate virtual environments efficaciously. We demonstrate our approach for synthesizing teleport graphs for common indoor scenes. By conducting a user study, we validate the efficacy and desirability of navigating virtual environments via the synthesized teleport graphs. We also extend our approach to cope with different constraints, user preferences, and practical scenarios.
VR social copresence with light field displays
Authors: Matsuda Nathan , Wheelwright Brian , Hegland Joel , Lanman Douglas
Published: December 2021

As virtual reality (VR) devices become increasingly commonplace, asymmetric interactions between people with and without headsets are becoming more frequent. Existing video pass-through VR headsets solve one side of these asymmetric interactions by showing the user a live reconstruction of the outside world. This paper further advocates for
reverse pass-through VR
, wherein a three-dimensional view of the user's face and eyes is presented to any number of outside viewers in a perspective-correct manner using a light field display. Tying together research in social telepresence and copresence, autostereoscopic displays, and facial capture, reverse pass-through VR enables natural eye contact and other important non-verbal cues in a wider range of interaction scenarios, providing a path to potentially increase the utility and social acceptability of VR headsets in shared and public spaces.
Live speech portraits
Authors: Lu Yuanxun , Chai Jinxiang , Cao Xun
Published: December 2021

To the best of our knowledge, we first present a live system that generates personalized photorealistic talking-head animation only driven by audio signals at over 30 fps. Our system contains three stages. The first stage is a deep neural network that extracts deep audio features along with a manifold projection to project the features to the target person's speech space. In the second stage, we learn facial dynamics and motions from the projected audio features. The predicted motions include head poses and upper body motions, where the former is generated by an autoregressive probabilistic model which models the head pose distribution of the target person. Upper body motions are deduced from head poses. In the final stage, we generate conditional feature maps from previous predictions and send them with a candidate image set to an image-to-image translation network to synthesize photorealistic renderings. Our method generalizes well to wild audio and successfully synthesizes high-fidelity personalized facial details, e.g., wrinkles, teeth. Our method also allows explicit control of head poses. Extensive qualitative and quantitative evaluations, along with user studies, demonstrate the superiority of our method over state-of-the-art techniques.
TreePartNet
Authors: Liu Yanchao , Guo Jianwei , Benes Bedrich , Deussen Oliver , Zhang Xiaopeng , Huang Hui
Published: December 2021

We present
TreePartNet
, a neural network aimed at reconstructing tree geometry from point clouds obtained by scanning real trees. Our key idea is to learn a natural
neural decomposition
exploiting the assumption that a tree comprises locally cylindrical shapes. In particular, reconstruction is a two-step process. First, two networks are used to detect priors from the point clouds. One detects semantic branching points, and the other network is trained to learn a cylindrical representation of the branches. In the second step, we apply a neural merging module to reduce the cylindrical representation to a final set of generalized cylinders combined by branches. We demonstrate results of reconstructing realistic tree geometry for a variety of input models and with varying input point quality, e.g., noise, outliers, and incompleteness. We evaluate our approach extensively by using data from both synthetic and real trees and comparing it with alternative methods.
Aerial path planning for online real-time exploration and offline high-quality reconstruction of large-scale urban scenes
Authors: Liu Yilin , Cui Ruiqi , Xie Ke , Gong Minglun , Huang Hui
Published: December 2021

Existing approaches have shown that, through carefully planning flight trajectories, images captured by Unmanned Aerial Vehicles (UAVs) can be used to reconstruct high-quality 3D models for real environments. These approaches greatly simplify and cut the cost of large-scale urban scene reconstruction. However, to properly capture height discontinuities in urban scenes, all state-of-the-art methods require prior knowledge on scene geometry and hence, additional prepossessing steps are needed before performing the actual image acquisition flights. To address this limitation and to make urban modeling techniques even more accessible, we present
a real-time explore-and-reconstruct planning
algorithm that does not require any prior knowledge for the scenes. Using only captured 2D images, we estimate 3D bounding boxes for buildings on-the-fly and use them to guide online path planning for both scene exploration and building observation. Experimental results demonstrate that the aerial paths planned by our algorithm in realtime for unknown environments support reconstructing 3D models with comparable qualities and lead to shorter flight air time.
NeRFactor
Authors: Zhang Xiuming , Srinivasan Pratul P. , Deng Boyang , Debevec Paul , Freeman William T. , Barron Jonathan T.
Published: December 2021

We address the problem of recovering the shape and spatially-varying reflectance of an object from multi-view images (and their camera poses) of an object illuminated by one unknown lighting condition. This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties. The key to our approach, which we call Neural Radiance Factorization (NeRFactor), is to distill the volumetric geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020] representation of the object into a surface representation and then jointly refine the geometry while solving for the spatially-varying reflectance and environment lighting. Specifically, NeRFactor recovers 3D neural fields of surface normals, light visibility, albedo, and Bidirectional Reflectance Distribution Functions (BRDFs) without any supervision, using only a re-rendering loss, simple smoothness priors, and a data-driven BRDF prior learned from real-world BRDF measurements. By explicitly modeling light visibility, NeRFactor is able to separate shadows from albedo and synthesize realistic soft or hard shadows under arbitrary lighting conditions. NeRFactor is able to recover convincing 3D models for free-viewpoint relighting in this challenging and underconstrained capture setup for both synthetic and real scenes. Qualitative and quantitative experiments show that NeRFactor outperforms classic and deep learning-based state of the art across various tasks. Our videos, code, and data are available at people.csail.mit.edu/xiuming/projects/nerfactor/.
SketchHairSalon
Authors: Xiao Chufeng , Yu Deng , Han Xiaoguang , Zheng Youyi , Fu Hongbo
Published: December 2021

Recent deep generative models allow real-time generation of hair images from sketch inputs. Existing solutions often require a user-provided binary mask to specify a target hair shape. This not only costs users extra labor but also fails to capture complicated hair boundaries. Those solutions usually encode hair structures via orientation maps, which, however, are not very effective to encode complex structures. We observe that colored hair sketches already implicitly define target hair shapes as well as hair appearance and are more flexible to depict hair structures than orientation maps. Based on these observations, we present
SketchHairSalon
, a two-stage framework for generating realistic hair images directly from freehand sketches depicting desired hair structure and appearance. At the first stage, we train a network to predict a hair matte from an input hair sketch, with an optional set of non-hair strokes. At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte. To make the networks in the two stages aware of long-term dependency of strokes, we apply self-attention modules to them. To train these networks, we present a new dataset containing thousands of annotated hair sketch-image pairs and corresponding hair mattes. Two efficient methods for sketch completion are proposed to automatically complete repetitive braided parts and hair strokes, respectively, thus reducing the workload of users. Based on the trained networks and the two sketch completion strategies, we build an intuitive interface to allow even novice users to design visually pleasing hair images exhibiting various hair structures and appearance via freehand sketches. The qualitative and quantitative evaluations show the advantages of the proposed system over the existing or alternative solutions.
Interactive cutting and tearing in projective dynamics with progressive cholesky updates
Authors: Li Jing , Liu Tiantian , Kavan Ladislav , Chen Baoquan
Published: December 2021

We propose a new algorithm for updating a Cholesky factorization which speeds up Projective Dynamics simulations with topological changes. Our approach addresses an important limitation of the original Projective Dynamics, i.e., that topological changes such as cutting, fracturing, or tearing require full refactorization which compromises computation speed, especially in real-time applications. Our method progressively modifies the Cholesky factor of the system matrix in the global step instead of computing it from scratch. Only a small amount of overhead is added since most of the topological changes in typical simulations are continuous and gradual. Our method is based on the update and downdate routine in CHOLMOD, but unlike recent related work, supports dynamic sizes of the system matrix and the addition of new vertices. Our approach allows us to introduce clean cuts and perform interactive remeshing. Our experiments show that our method works particularly well in simulation scenarios involving cutting, tearing, and local remeshing operations.
Reproducing reality with a high-dynamic-range multi-focal stereo display
Authors: Zhong Fangcheng , Jindal Akshay , Yöntem Ali Özgür , Hanji Param , Watt Simon J. , Mantiuk Rafał K.
Published: December 2021

With well-established methods for producing photo-realistic results, the next big challenge of graphics and display technologies is to achieve perceptual realism --- producing imagery indistinguishable from real-world 3D scenes. To deliver all necessary visual cues for perceptual realism, we built a High-Dynamic-Range Multi-Focal Stereo Display that achieves high resolution, accurate color, a wide dynamic range, and most depth cues, including binocular presentation and a range of focal depth. The display and associated imaging system have been designed to capture and reproduce a small near-eye three-dimensional object and to allow for a direct comparison between virtual and real scenes. To assess our reproduction of realism and demonstrate the capability of the display and imaging system, we conducted an experiment in which the participants were asked to discriminate between a virtual object and its physical counterpart. Our results indicate that the participants can only detect the discrepancy with a probability of 0.44. With such a level of perceptual realism, our display apparatus can facilitate a range of visual experiments that require the highest fidelity of reproduction while allowing for the full control of the displayed stimuli.
Perceptual model for adaptive local shading and refresh rate
Authors: Jindal Akshay , Wolski Krzysztof , Myszkowski Karol , Mantiuk Rafał K.
Published: December 2021

When the rendering budget is limited by power or time, it is necessary to find the combination of rendering parameters, such as resolution and refresh rate, that could deliver the best quality. Variable-rate shading (VRS), introduced in the last generations of GPUs, enables fine control of the rendering quality, in which each 16×16 image tile can be rendered with a different ratio of shader executions. We take advantage of this capability and propose a new method for adaptive control of local shading and refresh rate. The method analyzes texture content, on-screen velocities, luminance, and effective resolution and suggests the refresh rate and a VRS state map that maximizes the quality of animated content under a limited budget. The method is based on the new content-adaptive metric of judder, aliasing, and blur, which is derived from the psychophysical models of contrast sensitivity. To calibrate and validate the metric, we gather data from literature and also collect new measurements of motion quality under variable shading rates, different velocities of motion, texture content, and display capabilities, such as refresh rate, persistence, and angular resolution. The proposed metric and adaptive shading method is implemented as a game engine plugin. Our experimental validation shows a substantial increase in preference of our method over rendering with a fixed resolution and refresh rate, and an existing motion-adaptive technique.
Foids
Authors: Ishiwaka Yuko , Zeng Xiao S. , Eastman Michael Lee , Kakazu Sho , Gross Sarah , Mizutani Ryosuke , Nakada Masaki
Published: December 2021

We present a bio-inspired fish simulation platform, which we call "Foids", to generate realistic synthetic datasets for an use in computer vision algorithm training. This is a first-of-its-kind synthetic dataset platform for fish, which generates all the 3D scenes just with a simulation. One of the major challenges in deep learning based computer vision is the preparation of the annotated dataset. It is already hard to collect a good quality video dataset with enough variations; moreover, it is a painful process to annotate a sufficiently large video dataset frame by frame. This is especially true when it comes to a fish dataset because it is difficult to set up a camera underwater and the number of fish (target objects) in the scene can range up to 30,000 in a fish cage on a fish farm. All of these fish need to be annotated with labels such as a bounding box or silhouette, which can take hours to complete manually, even for only a few minutes of video. We solve this challenge by introducing a realistic synthetic dataset generation platform that incorporates details of biology and ecology studied in the aquaculture field. Because it is a simulated scene, it is easy to generate the scene data with annotation labels from the 3D mesh geometry data and transformation matrix. To this end, we develop an automated fish counting system utilizing the part of synthetic dataset that shows comparable counting accuracy to human eyes, which reduces the time compared to the manual process, and reduces physical injuries sustained by the fish.
Integer coordinates for intrinsic geometry processing
Authors: Gillespie Mark , Sharp Nicholas , Crane Keenan
Published: December 2021

This paper describes a numerically robust data structure for encoding intrinsic triangulations of polyhedral surfaces. Many applications demand a correspondence between the intrinsic triangulation and the input surface, but existing data structures either rely on floating point values to encode correspondence, or do not support remeshing operations beyond basic edge flips. We instead provide an integer-based data structure that guarantees valid correspondence, even for meshes with near-degenerate elements. Our starting point is the framework of
normal coordinates
from geometric topology, which we extend to the broader set of operations needed for mesh processing (vertex insertion, edge splits,
etc.
). The resulting data structure can be used as a drop-in replacement for earlier schemes, automatically improving reliability across a wide variety of applications. As a stress test, we successfully compute an intrinsic Delaunay refinement and associated subdivision for all manifold meshes in the Thingi10k dataset. In turn, we can compute reliable and highly accurate solutions to partial differential equations even on extremely low-quality meshes.
Kaleidoscopic structured light
Authors: Ahn Byeongjoo , Gkioulekas Ioannis , Sankaranarayanan Aswin C.
Published: December 2021

Full surround 3D imaging for shape acquisition is essential for generating digital replicas of real-world objects. Surrounding an object we seek to scan with a kaleidoscope, that is, a configuration of multiple planar mirrors, produces an image of the object that encodes information from a combinatorially large number of virtual viewpoints. This information is practically useful for the full surround 3D reconstruction of the object, but cannot be used directly, as we do not know what virtual viewpoint each image pixel corresponds---the pixel label. We introduce a structured light system that combines a projector and a camera with a kaleidoscope. We then prove that we can accurately determine the labels of projector and camera pixels, for arbitrary kaleidoscope configurations, using the projector-camera epipolar geometry. We use this result to show that our system can serve as a multi-view structured light system with hundreds of virtual projectors and cameras. This makes our system capable of scanning complex shapes precisely and with full coverage. We demonstrate the advantages of the kaleidoscopic structured light system by scanning objects that exhibit a large range of shapes and reflectances.
Keypoint-driven line drawing vectorization via PolyVector flow
Authors: Puhachov Ivan , Neveu William , Chien Edward , Bessmeltsev Mikhail
Published: December 2021

Line drawing vectorization is a daily task in graphic design, computer animation, and engineering, necessary to convert raster images to a set of curves for editing and geometry processing. Despite recent progress in the area, automatic vectorization tools often produce spurious branches or incorrect connectivity around curve junctions; or smooth out sharp corners. These issues detract from the use of such vectorization tools, both from an aesthetic viewpoint and for feasibility of downstream applications (e.g., automatic coloring or inbetweening). We address these problems by introducing a novel line drawing vectorization algorithm that splits the task into three components: (1) finding keypoints, i.e., curve endpoints, junctions, and sharp corners; (2) extracting drawing topology, i.e., finding connections between keypoints; and (3) computing the geometry of those connections. We compute the optimal geometry of the connecting curves via a novel geometric flow ---
PolyVector Flow
--- that aligns the curves to the drawing, disambiguating directions around Y-, X-, and T-junctions. We show that our system robustly infers both the geometry and topology of detailed complex drawings. We validate our system both quantitatively and qualitatively, demonstrating that our method visually outperforms previous work.
FreeStyleGAN
Authors: Leimkühler Thomas , Drettakis George
Published: December 2021

Current Generative Adversarial Networks (GANs) produce photorealistic renderings of portrait images. Embedding real images into the latent space of such models enables high-level image editing. While recent methods provide considerable semantic control over the (re-)generated images, they can only generate a limited set of viewpoints and cannot explicitly control the camera. Such 3D camera control is required for 3D virtual and mixed reality applications. In our solution, we use a few images of a face to perform 3D reconstruction, and we introduce the notion of the GAN camera manifold, the key element allowing us to precisely define the range of images that the GAN can reproduce in a stable manner. We train a small face-specific neural implicit representation network to map a captured face to this manifold and complement it with a warping scheme to obtain free-viewpoint novel-view synthesis. We show how our approach - due to its precise camera control - enables the integration of a pre-trained StyleGAN into standard 3D rendering pipelines, allowing e.g., stereo rendering or consistent insertion of faces in synthetic 3D environments. Our solution proposes the first truly free-viewpoint rendering of realistic faces at interactive rates, using only a small number of casual photos as input, while simultaneously allowing semantic editing capabilities, such as facial expression or lighting changes.
EyelashNet
Authors: Xiao Qinjie , Zhang Hanyuan , Zhang Zhaorui , Wu Yiqian , Wang Luyuan , Jin Xiaogang , Jiang Xinwei , Yang Yong-Liang , Shao Tianjia , Zhou Kun
Published: December 2021

Eyelashes play a crucial part in the human facial structure and largely affect the facial attractiveness in modern cosmetic design. However, the appearance and structure of eyelashes can easily induce severe artifacts in high-fidelity multi-view 3D face reconstruction. Unfortunately it is highly challenging to remove eyelashes from portrait images using both traditional and learning-based matting methods due to the delicate nature of eyelashes and the lack of eyelash matting dataset. To this end, we present EyelashNet, the first eyelash matting dataset which contains 5,400 high-quality eyelash matting data captured from real world and 5,272 virtual eyelash matting data created by rendering avatars. Our work consists of a capture stage and an inference stage to automatically capture and annotate eyelashes instead of tedious manual efforts. The capture is based on a specifically-designed fluorescent labeling system. By coloring the eyelashes with a safe and invisible fluorescent substance, our system takes paired photos with colored and normal eyelashes by turning the equipped ultraviolet (UVA) flash on and off. We further correct the alignment between each pair of photos and use a novel alpha matte inference network to extract the eyelash alpha matte. As there is no prior eyelash dataset, we propose a progressive training strategy that progressively fuses captured eyelash data with virtual eyelash data to learn the latent semantics of real eyelashes. As a result, our method can accurately extract eyelash alpha mattes from fuzzy and self-shadow regions such as pupils, which is almost impossible by manual annotations. To validate the advantage of EyelashNet, we present a baseline method based on deep learning that achieves state-of-the-art eyelash matting performance with RGB portrait images as input. We also demonstrate that our work can largely benefit important real applications including high-fidelity personalized avatar and cosmetic design.
Generalized fluid carving with fast lattice-guided seam computation
Authors: Flynn Sean , Hart David , Morse Bryan , Holladay Seth , Egbert Parris
Published: December 2021

In this paper, we introduce a novel method for intelligently resizing a wide range of volumetric data including fluids. Fluid carving, the technique we build upon, only supported particle-based liquid data, and because it was based on image-based techniques, it was constrained to rectangular boundaries. We address these limitations to allow a much more versatile method for volumetric post-processing. By enclosing a region of interest in our lattice structure, users can retarget regions of a volume with non-rectangular boundaries and non-axis-aligned motion. Our approach generalizes to images, videos, liquids, meshes, and even previously unexplored domains such as fire and smoke. We also present a seam computation method that is significantly faster than the previous approach while maintaining the same level of quality, thus making our method more viable for production settings where post-processing workflows are vital.
Path graphs
Authors: Deng Xi , Hašan Miloš , Carr Nathan , Xu Zexiang , Marschner Steve
Published: December 2021

To render higher quality images from the samples generated by path tracing with a low sample count, we propose a novel path reuse approach that processes a fixed collection of paths to iteratively refine and improve radiance estimates throughout the scene. Our method operates on a
path graph
consisting of the union of the traced paths with additional neighbor edges inserted among clustered nearby vertices. Our approach refines the initial noisy radiance estimates via an aggregation operator, treating vertices within clusters as independent sampling techniques that can be combined using MIS. In a novel step, we also introduce a propagation operator to forward the refined estimates along the paths to successive bounces. We apply the aggregation and propagation operations to the graph iteratively, progressively refining the radiance values, converging to fixed-point radiance estimates with lower variance than the original ones. We also introduce a decorrelation (final gather) step, which uses information already in the graph and is cheap to compute, allowing us to combine the method with standard denoisers. Our approach is lightweight, in the sense that it can be easily plugged into any standard path tracer and neural final image denoiser. Furthermore, it is independent of scene complexity, as the graph size only depends on image resolution and average path depth. We demonstrate that our technique leads to realistic rendering results starting from as low as 1 path per pixel, even in complex indoor scenes dominated by multi-bounce indirect illumination.
Physically-based feature line rendering
Authors: West Rex
Published: December 2021

Feature lines visualize the shape and structure of 3D objects, and are an essential component of many non-photorealistic rendering styles. Existing feature line rendering methods, however, are only able to render feature lines in limited contexts, such as on immediately visible surfaces or in specular reflections. We present a novel, path-based method for feature line rendering that allows for the accurate rendering of feature lines in the presence of complex physical phenomena such as glossy reflection, depth-of-field, and dispersion. Our key insight is that feature lines can be modeled as view-dependent light sources. These light sources can be sampled as a part of
ordinary paths
, and seamlessly integrate into existing physically-based rendering methods. We illustrate the effectiveness of our method in several real-world rendering scenarios with a variety of different physical phenomena.
Sum-of-squares geometry processing
Authors: Marschner Zoë , Zhang Paul , Palmer David , Solomon Justin
Published: December 2021

Geometry processing presents a variety of difficult numerical problems, each seeming to require its own tailored solution. This breadth is largely due to the expansive list of
geometric primitives
, e.g., splines, triangles, and hexahedra, joined with an ever-expanding variety of
objectives
one might want to achieve with them. With the recent increase in attention toward
higher-order surfaces
, we can expect a variety of challenges porting existing solutions that work on triangle meshes to work on these more complex geometry types. In this paper, we present a framework for solving many core geometry processing problems on higher-order surfaces. We achieve this goal through sum-of-squares optimization, which transforms nonlinear polynomial optimization problems into sequences of convex problems whose complexity is captured by a single
degree
parameter. This allows us to solve a suite of problems on higher-order surfaces, such as continuous collision detection and closest point queries on curved patches, with only minor changes between formulations and geometries.
Neural frame interpolation for rendered content
Authors: Briedis Karlis Martins , Djelouah Abdelaziz , Meyer Mark , McGonigal Ian , Gross Markus , Schroers Christopher
Published: December 2021

The demand for creating rendered content continues to drastically grow. As it often is extremely computationally expensive and thus costly to render high-quality computer-generated images, there is a high incentive to reduce this computational burden. Recent advances in learning-based frame interpolation methods have shown exciting progress but still have not achieved the production-level quality which would be required to render fewer pixels and achieve savings in rendering times and costs. Therefore, in this paper we propose a method specifically targeted to achieve high-quality frame interpolation for rendered content. In this setting, we assume that we have full input for every
n
-th frame in addition to auxiliary feature buffers that are cheap to evaluate (e.g. depth, normals, albedo) for every frame. We propose solutions for leveraging such auxiliary features to obtain better motion estimates, more accurate occlusion handling, and to correctly reconstruct non-linear motion between keyframes. With this, our method is able to significantly push the state-of-the-art in frame interpolation for rendered content and we are able to obtain production-level quality results.
Differentiable surface triangulation
Authors: Rakotosaona Marie-Julie , Aigerman Noam , Mitra Niloy J. , Ovsjanikov Maks , Guerrero Paul
Published: December 2021

Triangle meshes remain the most popular data representation for surface geometry. This ubiquitous representation is essentially a hybrid one that decouples continuous
vertex locations
from the discrete topological
triangulation.
Unfortunately, the combinatorial nature of the triangulation prevents taking derivatives over the space of possible meshings of any given surface. As a result, to date, mesh processing and optimization techniques have been unable to truly take advantage of modular gradient descent components of modern optimization frameworks. In this work, we present a
differentiable surface triangulation
that enables optimization for any per-vertex or per-face differentiable objective function over the space of underlying surface triangulations. Our method builds on the result that
any
2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation. We translate this result into a computational algorithm by proposing a soft relaxation of the classical weighted Delaunay triangulation and optimizing over vertex weights and vertex locations. We extend the algorithm to 3D by decomposing shapes into developable sets and differentiably meshing each set with suitable boundary constraints. We demonstrate the efficacy of our method on various planar and surface meshes on a range of difficult-to-optimize objective functions. Our code can be found online: https://github.com/mrakotosaon/diff-surface-triangulation.
Learning to cluster for rendering with many lights
Authors: Wang Yu-Chen , Wu Yu-Ting , Li Tzu-Mao , Chuang Yung-Yu
Published: December 2021

We present an unbiased online Monte Carlo method for rendering with many lights. Our method adapts both the hierarchical light clustering and the sampling distribution to our collected samples. Designing such a method requires us to make clustering decisions under noisy observation, and making sure that the sampling distribution adapts to our target. Our method is based on two key ideas: a coarse-to-fine clustering scheme that can find good clustering configurations even with noisy samples, and a discrete stochastic successive approximation method that starts from a prior distribution and provably converges to a target distribution. We compare to other state-of-the-art light sampling methods, and show better results both numerically and visually.
Monte Carlo denoising via auxiliary feature guided self-attention
Authors: Yu Jiaqi , Nie Yongwei , Long Chengjiang , Xu Wenju , Zhang Qing , Li Guiqing
Published: December 2021

While self-attention has been successfully applied in a variety of natural language processing and computer vision tasks, its application in Monte Carlo (MC) image denoising has not yet been well explored. This paper presents a self-attention based MC denoising deep learning network based on the fact that self-attention is essentially non-local means filtering in the embedding space which makes it inherently very suitable for the denoising task. Particularly, we modify the standard self-attention mechanism to an auxiliary feature guided self-attention that considers the by-products (e.g., auxiliary feature buffers) of the MC rendering process. As a critical prerequisite to fully exploit the performance of self-attention, we design a multi-scale feature extraction stage, which provides a rich set of raw features for the later self-attention module. As self-attention poses a high computational complexity, we describe several ways that accelerate it. Ablation experiments validate the necessity and effectiveness of the above design choices. Comparison experiments show that the proposed self-attention based MC denoising method outperforms the current state-of-the-art methods.
Interactive all-hex meshing via cuboid decomposition
Authors: Li Lingxiao , Zhang Paul , Smirnov Dmitriy , Abulnaga S. Mazdak , Solomon Justin
Published: December 2021

Standard PolyCube-based hexahedral (hex) meshing methods aim to deform the input domain into an axis-aligned PolyCube volume with integer corners; if this deformation is bijective, then applying the inverse map to the voxelized PolyCube yields a valid hex mesh. A key challenge in these methods is to maintain the bijectivity of the PolyCube deformation, thus reducing the robustness of these algorithms. In this work, we present an interactive pipeline for hex meshing that sidesteps this challenge by using a new representation of PolyCubes as unions of cuboids. We begin by deforming the input tetrahedral mesh into a near-PolyCube domain whose faces are loosely aligned to the major axis directions. We then build a PolyCube by optimizing the layout of a set of cuboids with user guidance to closely fit the deformed domain. Finally, we construct an inversion-free pullback map from the voxelized PolyCube to the input domain while optimizing for mesh quality metrics. We allow extensive user control over each stage, such as editing the voxelized PolyCube, positioning surface vertices, and exploring the trade-off among competing quality metrics, while also providing automatic alternatives. We validate our method on over one hundred shapes, including models that are challenging for past PolyCube-based and frame-field-based methods. Our pipeline reliably produces hex meshes with quality on par with or better than state-of-the-art. We additionally conduct a user study with 21 participants in which the majority prefer hex meshes they make using our tool to the ones from automatic state-of-the-art methods. This demonstrates the need for intuitive interactive hex meshing tools where the user can dictate the priorities of their mesh.
Transflower
Authors: Valle-Pérez Guillermo , Henter Gustav Eje , Beskow Jonas , Holzapfel Andre , Oudeyer Pierre-Yves , Alexanderson Simon
Published: December 2021

Dance requires skillful composition of complex movements that follow rhythmic, tonal and timbral features of music. Formally, generating dance conditioned on a piece of music can be expressed as a problem of modelling a high-dimensional continuous motion signal, conditioned on an audio signal. In this work we make two contributions to tackle this problem. First, we present a novel probabilistic autoregressive architecture that models the distribution over future poses with a normalizing flow conditioned on previous poses as well as music context, using a multimodal transformer encoder. Second, we introduce the currently largest 3D dance-motion dataset, obtained with a variety of motion-capture technologies, and including both professional and casual dancers. Using this dataset, we compare our new model against two baselines, via objective metrics and a user study, and show that both the ability to model a probability distribution, as well as being able to attend over a large motion and music context are necessary to produce interesting, diverse, and realistic dance that matches the music.
Bimolecular Chemistry in the Ultracold Regime
Authors: Liu Yu , Ni Kang-Kuen
Published: December 2021

Advances in atomic, molecular, and optical physics techniques allowed the cooling of simple molecules down to the ultracold regime ([Formula: see text]1 mK) and opened opportunities to study chemical reactions with unprecedented levels of control. This review covers recent developments in studying bimolecular chemistry at ultralow temperatures. We begin with a brief overview of methods for producing, manipulating, and detecting ultracold molecules. We then survey experimental works that exploit the controllability of ultracold molecules to probe and modify their long-range interactions. Further combining the use of physical chemistry techniques such as mass spectrometry and ion imaging significantly improved the detection of ultracold reactions and enabled explorations of their dynamics in the short range. We discuss a series of studies on the reaction KRb + KRb → K2 + Rb2 initiated below 1 μK, including the direct observation of a long-lived complex, the demonstration of product rotational state control via conserved nuclear spins, and a test of the statistical model using the complete quantum state distribution of the products.
Electrochemical Modeling of Iodide Oxidation in Metal-Halide Molten Salts
Authors: Lee Rose Y. , Percival Stephen J. , Small Leo J.
Published: December 2021

The oxidation of iodide in NaI-AlBr3, NaI-AlCl3, and NaI-GaCl3 molten salts was analyzed using simulation software to extract relevant kinetic parameters. The experimental oxidation potentials were ordered AlCl3 < AlBr3 < GaCl3, with higher oxidation potentials correlating with softer Lewis acidity of the metal halide. An iodide oxidation and metal halide speciation model was developed and simulated to fit the electrochemical response, enabling determination of electrochemical charge transfer parameters and chemical equilibrium constants. NaI-AlBr3 displayed the fastest electron transfer rates yet showed the lowest current densities. All salts revealed smaller than expected current densities, explained by equilibrium between various species, where some are not electrochemically active at the studied potentials. These equilibrium reactions are due to the various metal halide species, controlling the reactant concentration of iodide and the resultant current. We hypothesize the electrochemically active iodide species, present as a metal halide monomer (MX3I−), is decreased dramatically from the expected concentration, sequestered as a more stable metal halide dimer species (M2X6I−) with a higher oxidation potential. Traditional Tafel analysis of the experimental data supports the validity of the simulations. These results increase understanding of iodide oxidation in low-temperature Lewis acidic molten salts and inform task-specific molten salt design.