933 research outputs found
Reading: Amit Majmudar
No full textBecause of COVID-19 this event is canceled.
Amit Majmudar, a multi-genre author and translator, offers a Sacred Arts Festival reading that explores the concept of Building Bridges.
Co-sponsored by the Department of English and the Sacred Arts Festival
Exploring young students creativity: The effect of model eliciting activities
No full textThe aim of this paper is to show how engaging students in real-life mathematical situations can stimulate their mathematical creative thinking. We analyzed the mathematical modeling of two girls, aged 10 and 13 years, as they worked on an authentic task involving the selection of a track team. The girls displayed several modeling cycles that revealed their thinking processes, as well as cognitive and affective features that may serve as the foundation for a methodology that uses model-eliciting activities to promote the mathematical creative process
sj-pdf-1-amp-10.1177_25152459221147250 – Supplemental material for iCatcher+: Robust and Automated Annotation of Infants’ and Young Children’s Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies
No full textSupplemental material, sj-pdf-1-amp-10.1177_25152459221147250 for iCatcher+: Robust and Automated Annotation of Infants’ and Young Children’s Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies by Yotam Erel, Katherine Adams Shannon, Junyi Chu, Kim Scott, Melissa Kline Struhl, Peng Cao, Xincheng Tan, Peter Hart, Gal Raz, Sabrina Piccolo, Catherine Mei, Christine Potter, Sagi Jaffe-Dax, Casey Lew-Williams, Joshua Tenenbaum, Katherine Fairchild, Amit Bermano and Shari Liu in Advances in Methods and Practices in Psychological Science</p
Physically-based Analytical Erosion for fast Terrain Generation
No full textTerrain generation methods have long been divided between procedural and physically-based. Procedural methods build upon the fast evaluation of a mathematical function but suffer from a lack of geological consistency, while physically-based simulation enforces this consistency at the cost of thousands of iterations unraveling the history of the landscape. In particular, the simulation of the competition between tectonic uplift and fluvial erosion expressed by the stream power law raised recent interest in computer graphics as this allows the generation and control of consistent large-scale mountain ranges, albeit at the cost of a lengthy simulation. In this paper, we explore the analytical solutions of the stream power law and propose a method that is both physically-based and procedural, allowing fast and consistent large-scale terrain generation. In our approach, time is no longer the stopping criterion of an iterative process but acts as the parameter of a mathematical function, a slider that controls the aging of the input terrain from a subtle erosion to the complete replacement by a fully formed mountain range. While analytical solutions have been proposed by the geomorphology community for the 1D case, extending them to a 2D heightmap proves challenging. We propose an efficient implementation of the analytical solutions with a multigrid accelerated iterative process and solutions to incorporate landslides and hillslope processes – two erosion factors that complement the stream power law.Computer Graphics ForumSimulating Natural Phenomena43
Real-Time Underwater Spectral Rendering
No full textThe light field in an underwater environment is characterized by complex multiple scattering interactions and wavelengthdependent attenuation, requiring significant computational resources for the simulation of underwater scenes. We present a novel approach that makes it possible to simulate multi-spectral underwater scenes, in a physically-based manner, in real time. Our key observation is the following: In the vertical direction, the steady decay in irradiance as a function of depth is characterized by the diffuse downwelling attenuation coefficient, which oceanographers routinely measure for different types of waters. We rely on a database of such real-world measurements to obtain an analytical approximation to the Radiative Transfer Equation, allowing for real-time spectral rendering with results comparable to Monte Carlo ground-truth references, in a fraction of the time. We show results simulating underwater appearance for the different optical water types, including volumetric shadows and dynamic, spatially varying lighting near the water surface.Computer Graphics ForumRendering Natural Phenomena43
3D Reconstruction and Semantic Modeling of Eyelashes
No full textHigh-fidelity digital human modeling has become crucial in various applications, including gaming, visual effects and virtual reality. Despite the significant impact of eyelashes on facial aesthetics, their reconstruction and modeling have been largely unexplored. In this paper, we introduce the first data-driven generative model of eyelashes based on semantic features. This model is derived from real data by introducing a new 3D eyelash reconstruction method based on multi-view images. The reconstructed data is made available which constitutes the first dataset of 3D eyelashes ever published. Through an innovative extraction process, we determine the features of any set of eyelashes, and present detailed descriptive statistics of human eyelashes shapes. The proposed eyelashes model, which exclusively relies on semantic parameters, effectively captures the appearance of a set of eyelashes. Results show that the proposed model enables interactive, intuitive and realistic eyelashes modeling for non-experts, enriching avatar creation and synthetic data generation pipelines.Computer Graphics ForumFace Modeling and Reconstruction43
Learning to Stabilize Faces
No full textNowadays, it is possible to scan faces and automatically register them with high quality. However, the resulting face meshes often need further processing: we need to stabilize them to remove unwanted head movement. Stabilization is important for tasks like game development or movie making which require facial expressions to be cleanly separated from rigid head motion. Since manual stabilization is labor-intensive, there have been attempts to automate it. However, previous methods remain impractical: they either still require some manual input, produce imprecise alignments, rely on dubious heuristics and slow optimization, or assume a temporally ordered input. Instead, we present a new learning-based approach that is simple and fully automatic. We treat stabilization as a regression problem: given two face meshes, our network directly predicts the rigid transform between them that brings their skulls into alignment. We generate synthetic training data using a 3D Morphable Model (3DMM), exploiting the fact that 3DMM parameters separate skull motion from facial skin motion. Through extensive experiments we show that our approach outperforms the state-of-the-art both quantitatively and qualitatively on the tasks of stabilizing discrete sets of facial expressions as well as dynamic facial performances. Furthermore, we provide an ablation study detailing the design choices and best practices to help others adopt our approach for their own uses.Computer Graphics ForumFace Modeling and Reconstruction43
Physically-Based Lighting for 3D Generative Models of Cars
No full textRecent work has demonstrated that Generative Adversarial Networks (GANs) can be trained to generate 3D content from 2D image collections, by synthesizing features for neural radiance field rendering. However, most such solutions generate radiance, with lighting entangled with materials. This results in unrealistic appearance, since lighting cannot be changed and view-dependent effects such as reflections do not move correctly with the viewpoint. In addition, many methods have difficulty for full, 360? rotations, since they are often designed for mainly front-facing scenes such as faces. We introduce a new 3D GAN framework that addresses these shortcomings, allowing multi-view coherent 360? viewing and at the same time relighting for objects with shiny reflections, which we exemplify using a car dataset. The success of our solution stems from three main contributions. First, we estimate initial camera poses for a dataset of car images, and then learn to refine the distribution of camera parameters while training the GAN. Second, we propose an efficient Image-Based Lighting model, that we use in a 3D GAN to generate disentangled reflectance, as opposed to the radiance synthesized in most previous work. The material is used for physically-based rendering with a dataset of environment maps. Third, we improve the 3D GAN architecture compared to previous work and design a careful training strategy that allows effective disentanglement. Our model is the first that generate a variety of 3D cars that are multi-view consistent and that can be relit interactively with any environment map.Computer Graphics ForumNeural 3D Shape Synthesis43
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