bluuebunny/crossref_metadata_2025_split huggingface dataset:
Silicone sling frontalis suspension for congenital ptosis: Outcome of 174 consecutive cases
Authors: Landau Prat Daphna , Ramakrishnan Meera , Zhao Cindy , Revere Karen , Katowitz William R. , Katowitz James A. 
Published: May 2022

AbstractBackgroundFrontalis suspension (FS) is the treatment of choice in congenital ptosis with poor levator function. We report the surgical outcome of FS using a double triangle configuration of silicone slings in children.MethodsA retrospective cohort study of all paediatric patients with simple myogenic congenital ptosis repaired with FS over a 12‐year period (2009–2020). Each silicone sling was secured by simple knots. Pre‐ and post‐operative margin reflex distance (MRD1) measurements were determined from clinical photographs using ImageJ Software. Main outcome measures were improvement in eyelid height, eyelid asymmetry, reoperation rate and timing.ResultsOne hundred and thirty nine patients (174 eyes) were included, with 35 (25%) having bilateral surgery. Mean (±SD) age was 1.4 ± 1.9 years. Mean follow up time was 32 ± 20.5 months. Sixteen patients (11%) had a history of previous ptosis repair surgery. Mean MRD1 improved by an average of 1.5 mm. The final MRD1 in the group of patients who did not have a second procedure was a mean of 2.1 mm. The MRD1 difference between both eyes in all unilateral cases improved from 2.5 mm preoperatively to 1.2 mm at final visit (p < 0.001). In the 123 cases without a history of previous ptosis surgery, repeat ptosis repair was performed in 37 (30%) patients, 34.9 ± 19.9 months after the initial procedure. Overall, repeat repair was performed in 47 patients (34%).ConclusionsDouble triangle silicone sling frontalis suspension has a favourable outcome in two‐thirds of paediatric patients with simple myogenic congenital ptosis. Failed cases can be addressed with a second repair, using either autogenous fascia lata or a second silicone sling.
Progressive Denoising of Monte Carlo Rendered Images
Authors: Firmino Arthur , Frisvad Jeppe Revall , Jensen Henrik Wann 
Published: May 2022

AbstractImage denoising based on deep learning has become a powerful tool to accelerate Monte Carlo rendering. Deep learning techniques can produce smooth images using a low sample count. Unfortunately, existing deep learning methods are biased and do not converge to the correct solution as the number of samples increase. In this paper, we propose a progressive denoising technique that aims to use denoising only when it is beneficial and to reduce its impact at high sample counts. We use Stein's unbiased risk estimate (SURE) to estimate the error in the denoised image, and we combine this with a neural network to infer a per‐pixel mixing parameter. We further augment this network with confidence intervals based on classical statistics to ensure consistency and convergence of the final denoised image. Our results demonstrate that our method is consistent and that it improves existing denoising techniques. Furthermore, it can be used in combination with existing high quality denoisers to ensure consistency. In addition to being asymptotically unbiased, progressive denoising is particularly good at preserving fine details that would otherwise be lost with existing denoisers.
Deep Reconstruction of 3D Smoke Densities from Artist Sketches
Authors: Kim Byungsoo , Huang Xingchang , Wuelfroth Laura , Tang Jingwei , Cordonnier Guillaume , Gross Markus , Solenthaler Barbara 
Published: May 2022

AbstractCreative processes of artists often start with hand‐drawn sketches illustrating an object. Pre‐visualizing these keyframes is especially challenging when applied to volumetric materials such as smoke. The authored 3D density volumes must capture realistic flow details and turbulent structures, which is highly non‐trivial and remains a manual and time‐consuming process. We therefore present a method to compute a 3D smoke density field directly from 2D artist sketches, bridging the gap between early‐stage prototyping of smoke keyframes and pre‐visualization. From the sketch inputs, we compute an initial volume estimate and optimize the density iteratively with an updater CNN. Our differentiable sketcher is embedded into the end‐to‐end training, which results in robust reconstructions. Our training data set and sketch augmentation strategy are designed such that it enables general applicability. We evaluate the method on synthetic inputs and sketches from artists depicting both realistic smoke volumes and highly non‐physical smoke shapes. The high computational performance and robustness of our method at test time allows interactive authoring sessions of volumetric density fields for rapid prototyping of ideas by novice users.
Interactive Editing of Discrete Chebyshev Nets
Authors: Li Rui‐Zeng , Guo Jia‐Peng , Wang Qi , Chai Shuangming , Liu Ligang , Fu Xiao‐Ming 
Published: May 2022

AbstractWe propose an interactive method to edit a discrete Chebyshev net, which is a quad mesh with edges of the same length. To ensure that the edited mesh is always a discrete Chebyshev net, the maximum difference of all edge lengths should be zero during the editing process. Hence, we formulate an objective function using ℓp‐norm (p > 2) to force the maximum length deviation to approach zero in practice. To optimize the nonlinear and non‐convex objective function interactively and efficiently, we develop a novel second‐order solver. The core of the solver is to construct a new convex majorizer for our objective function to achieve fast convergence. We present two acceleration strategies to further reduce the optimization time, including adaptive p change and adaptive variables reduction. A large number of experiments demonstrate the capability and feasibility of our method for interactively editing complex discrete Chebyshev nets.
CAST: Character labeling in Animation using Self‐supervision by Tracking
Authors: Nir Oron , Rapoport Gal , Shamir Ariel 
Published: May 2022

AbstractCartoons and animation domain videos have very different characteristics compared to real‐life images and videos. In addition, this domain carries a large variability in styles. Current computer vision and deep‐learning solutions often fail on animated content because they were trained on natural images. In this paper we present a method to refine a semantic representation suitable for specific animated content. We first train a neural network on a large‐scale set of animation videos and use the mapping to deep features as an embedding space. Next, we use self‐supervision to refine the representation for any specific animation style by gathering many examples of animated characters in this style, using a multi‐object tracking. These examples are used to define triplets for contrastive loss training. The refined semantic space allows better clustering of animated characters even when they have diverse manifestations. Using this space we can build dictionaries of characters in an animation videos, and define specialized classifiers for specific stylistic content (e.g., characters in a specific animation series) with very little user effort. These classifiers are the basis for automatically labeling characters in animation videos. We present results on a collection of characters in a variety of animation styles.Code and resources are available at: https://github.com/oronnir/CAST.
MaterIA: Single Image High‐Resolution Material Capture in the Wild
Authors: Martin Rosalie , Roullier Arthur , Rouffet Romain , Kaiser Adrien , Boubekeur Tamy 
Published: May 2022

AbstractWe propose a hybrid method to reconstruct a physically‐based spatially varying BRDF from a single high resolution picture of an outdoor surface captured under natural lighting conditions with any kind of camera device. Relying on both deep learning and explicit processing, our PBR material acquisition handles the removal of shades, projected shadows and specular highlights present when capturing a highly irregular surface and enables to properly retrieve the underlying geometry. To achieve this, we train two cascaded U‐Nets on physically‐based materials, rendered under various lighting conditions, to infer the spatially‐varying albedo and normal maps. Our network processes relatively small image tiles (512 × 512pixels) and we propose a solution to handle larger image resolutions by solving a Poisson system across these tiles. We complete this pipeline with analytical solutions to reconstruct height, roughness and ambient occlusion.
OutCast: Outdoor Single‐image Relighting with Cast Shadows
Authors: Griffiths David , Ritschel Tobias , Philip Julien 
Published: May 2022

AbstractWe propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accounting for shading and global effects such the sun light color and clouds. Previous solutions for this problem rely on reconstructing occluder geometry, e. g., using multi‐view stereo, which requires many images of the scene. Instead, in this work we make use of a noisy off‐the‐shelf single‐image depth map estimation as a source of geometry. Whilst this can be a good guide for some lighting effects, the resulting depth map quality is insufficient for directly ray‐tracing the shadows. Addressing this, we propose a learned image space ray‐marching layer that converts the approximate depth map into a deep 3D representation that is fused into occlusion queries using a learned traversal. Our proposed method achieves, for the first time, state‐of‐the‐art relighting results, with only a single image as input. For supplementary material visit our project page at: dgriffiths.uk/outcast.
Procedural Roof Generation From a Single Satellite Image
Authors: Zhang Xiaowei , Aliaga Daniel 
Published: May 2022

AbstractUrban procedural modeling has benefited from recent advances in deep learning and computer graphics. However, few, if any, approaches have automatically produced procedural building roof models from a single overhead satellite image. Large‐scale roof modeling is important for a variety of applications in urban content creation and in urban planning (e.g., solar panel planning, heating/cooling/rainfall modeling). While the allure of modeling only from satellite images is clear, unfortunately structures obtained from the satellite images are often in low‐resolution, noisy and heavily occluded, thus getting a clean and complete view of urban structures is difficult. In this paper, we present a framework that exploits the inherent structure present in man‐made buildings and roofs by explicitly identifying the compact space of potential building shapes and roof structures. Then, we utilize this relatively compact space with a two‐component solution combining procedural modeling and deep learning. Specifically, we use a building decomposition component to separate the building into roof parts and predict regularized building footprints in a procedural format, and use a roof ridge detection component to refine the individual roof parts by estimating the procedural roof ridge parameters. Our qualitative and quantitative assessments over multiple satellite datasets show that our method outperforms various state‐of‐the‐art methods.
Differentiable 3D CAD Programs for Bidirectional Editing
Authors: Cascaval D. , Shalah M. , Quinn P. , Bodik R. , Agrawala M. , Schulz A. 
Published: May 2022

AbstractModern CAD tools represent 3D designs not only as geometry, but also as a program composed of geometric operations, each of which depends on a set of parameters. Program representations enable meaningful and controlled shape variations via parameter changes. However, achieving desired modifications solely through parameter editing is challenging when CAD models have not been explicitly authored to expose select degrees of freedom in advance. We introduce a novel bidirectional editing system for 3D CAD programs. In addition to editing the CAD program, users can directly manipulate 3D geometry and our system infers parameter updates to keep both representations in sync. We formulate inverse edits as a set of constrained optimization objectives, returning plausible updates to program parameters that both match user intent and maintain program validity. Our approach implements an automatically differentiable domain‐specific language for CAD programs, providing derivatives for this optimization to be performed quickly on any expressed program. Our system enables rapid, interactive exploration of a constrained 3D design space by allowing users to manipulate the program and geometry interchangeably during design iteration. While our approach is not designed to optimize across changes in geometric topology, we show it is expressive and performant enough for users to produce a diverse set of design variants, even when the CAD program contains a relatively large number of parameters.
Worst‐Case Rigidity Analysis and Optimization for Assemblies with Mechanical Joints
Authors: Liu Zhenyuan , Hu Jingyu , Xu Hao , Song Peng , Zhang Ran , Bickel Bernd , Fu Chi‐Wing 
Published: May 2022

AbstractWe study structural rigidity for assemblies with mechanical joints. Existing methods identify whether an assembly is structurally rigid by assuming parts are perfectly rigid. Yet, an assembly identified as rigid may not be that “rigid” in practice, and existing methods cannot quantify how rigid an assembly is. We address this limitation by developing a new measure, worst‐case rigidity, to quantify the rigidity of an assembly as the largest possible deformation that the assembly undergoes for arbitrary external loads of fixed magnitude. Computing worst‐case rigidity is non‐trivial due to non‐rigid parts and different joint types. We thus formulate a new computational approach by encoding parts and their connections into a stiffness matrix, in which parts are modeled as deformable objects and joints as soft constraints. Based on this, we formulate worst‐case rigidity analysis as an optimization that seeks the worst‐case deformation of an assembly for arbitrary external loads, and solve the optimization problem via an eigenanalysis. Furthermore, we present methods to optimize the geometry and topology of various assemblies to enhance their rigidity, as guided by our rigidity measure. In the end, we validate our method on a variety of assembly structures with physical experiments and demonstrate its effectiveness by designing and fabricating several structurally rigid assemblies.
Interaction Fields: Intuitive Sketch‐based Steering Behaviors for Crowd Simulation
Authors: Colas A. , van Toll W. , Zibrek K. , Hoyet L. , Olivier A.‐H. , Pettré J. 
Published: May 2022

AbstractThe real‐time simulation of human crowds has many applications. In a typical crowd simulation, each person ('agent') in the crowd moves towards a goal while adhering to local constraints. Many algorithms exist for specific local ‘steering’ tasks such as collision avoidance or group behavior. However, these do not easily extend to completely new types of behavior, such as circling around another agent or hiding behind an obstacle. They also tend to focus purely on an agent's velocity without explicitly controlling its orientation. This paper presents a novel sketch‐based method for modelling and simulating many steering behaviors for agents in a crowd. Central to this is the concept of aninteraction field(IF): a vector field that describes the velocities or orientations that agents should use around a given ‘source’ agent or obstacle. An IF can also change dynamically according to parameters, such as the walking speed of the source agent. IFs can be easily combined with other aspects of crowd simulation, such as collision avoidance. Using an implementation of IFs in a real‐time crowd simulation framework, we demonstrate the capabilities of IFs in various scenarios. This includes game‐like scenarios where the crowd responds to a user‐controlled avatar. We also present an interactive tool that computes an IF based on input sketches. This IF editor lets users intuitively and quickly design new types of behavior, without the need for programming extra behavioral rules. We thoroughly evaluate the efficacy of the IF editor through a user study, which demonstrates that our method enables non‐expert users to easily enrich any agent‐based crowd simulation with new agent interactions.
A Survey of Non‐Rigid 3D Registration
Authors: Deng Bailin , Yao Yuxin , Dyke Roberto M. , Zhang Juyong 
Published: May 2022

AbstractNon‐rigid registration computes an alignment between a source surface with a target surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing technologies that can measure time‐varying surfaces, non‐rigid registration has been applied for the acquisition of deformable shapes and has a wide range of applications. This survey presents a comprehensive review of non‐rigid registration methods for 3D shapes, focusing on techniques related to dynamic shape acquisition and reconstruction. In particular, we review different approaches for representing the deformation field, and the methods for computing the desired deformation. Both optimization‐based and learning‐based methods are covered. We also review benchmarks and datasets for evaluating non‐rigid registration methods, and discuss potential future research directions.
Advances in Neural Rendering
Authors: Tewari A. , Thies J. , Mildenhall B. , Srinivasan P. , Tretschk E. , Yifan W. , Lassner C. , Sitzmann V. , Martin‐Brualla R. , Lombardi S. , Simon T. , Theobalt C. , Nießner M. , Barron J. T. , Wetzstein G. , Zollhöfer M. , Golyanik V. 
Published: May 2022

AbstractSynthesizing photo‐realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are generated using rendering algorithms such as rasterization or ray tracing, which take specifically defined representations of geometry and material properties as input. Collectively, these inputs define the actual scene and what is rendered, and are referred to as the scene representation (where a scene consists of one or more objects). Example scene representations are triangle meshes with accompanied textures (e.g., created by an artist), point clouds (e.g., from a depth sensor), volumetric grids (e.g., from a CT scan), or implicit surface functions (e.g., truncated signed distance fields). The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering. Neural rendering is closely related, and combines ideas from classical computer graphics and machine learning to create algorithms for synthesizing images from real‐world observations. Neural rendering is a leap forward towards the goal of synthesizing photo‐realistic image and video content. In recent years, we have seen immense progress in this field through hundreds of publications that show different ways to inject learnable components into the rendering pipeline. This state‐of‐the‐art report on advances in neural rendering focuses on methods that combine classical rendering principles with learned 3D scene representations, often now referred to as neural scene representations. A key advantage of these methods is that they are 3D‐consistent by design, enabling applications such as novel viewpoint synthesis of a captured scene. In addition to methods that handle static scenes, we cover neural scene representations for modeling non‐rigidly deforming objects and scene editing and composition. While most of these approaches are scene‐specific, we also discuss techniques that generalize across object classes and can be used for generative tasks. In addition to reviewing these state‐of‐the‐art methods, we provide an overview of fundamental concepts and definitions used in the current literature. We conclude with a discussion on open challenges and social implications.
General population normative scores for interpreting the BODY‐Q
Authors: Dalaei Farima , de Vries Claire E. E. , Poulsen Lotte , Kaur Manraj N. , Pfob André , Mou Danny , Jacobsen Amalie L. , Repo Jussi P. , Salzillo Rosa , Opyrchal Jakub , Klassen Anne F. , Sørensen Jens Ahm , Pusic Andrea L. 
Published: May 2022

SummaryThe BODY‐Q is a patient‐reported outcome measure used to assess outcomes in patients undergoing weight loss and/or body contouring surgery (BC) following massive weight loss. Normative values for the BODY‐Q are needed to improve data interpretation and enable comparison. Thus, the aim of this study was to determine normative values for the BODY‐Q. Participants were recruited internationally through two crowdsourcing platforms. The participants were invited to complete the BODY‐Q scales through an URL link provided within the crowdsourcing platforms. General linear analyses were performed to compare normative means between countries and continents adjusted for relevant covariates. Normative reference values were stratified by age, body mass index (BMI), and gender. The BODY‐Q was completed by 4051 (2052 North American and 1999 European) participants. The mean age was 36 years (±14.7 SD) and ranged from 17 to 76 years, the mean BMI was 26.4 (±6.7 SD) kg/m2, and the sample consisted of 1996 (49.3%) females and 2023 (49.9%) males. Younger age and higher BMI were negatively associated with all BODY‐Q scales (p < .001). This study provides normative values for the BODY‐Q scales to aid in the interpretation of BODY‐Q scores in research and clinical practise. These values enable us to understand the impact of weight loss and BC on patients' lives.
Coloration with nanoparticles: Scope for developing simultaneous colouring and functional properties onto textile surfaces—a short review
Authors: Kundu Chanchal Kumar , Hossen Md. Tanvir , Saha Rajib 
Published: May 2022

AbstractTraditional textile dyeing using synthetic organic dyes is a hazardous method and requires the appropriate treatment of wastewater (i.e., effluent) after the dyeing process. Over time, numerous more environmentally‐friendly dyeing techniques have been innovated as a counter dyeing technique against this traditional dyeing method. Among them, nano‐coloration technology is a revolutionary innovation where textile dyeing can be carried out using solely some typical nanoparticles rather than using an organic dye, which is relatively more sustainable and green, compared to the traditional method. To date, several novel nanoparticles have been considered for a varied class of textiles and they have shown great potential in developing colour and numerous functional properties, namely antimicrobial, hydrophilic, self‐cleaning, ultraviolet (UV) protection, flame retardance and so on. Here, in this review, the recent advances regarding the uses of these nanoparticles as a novel class of colourants for the textile substrates are discussed while their related challenges and future opportunities are also analysed.