CG (computer graphics) is a popular field of CS (computer Science), but many students find this topic difficult due to it requiring a large number of skills, such as mathematics, programming, geometric reasoning, and ...
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ISBN:
(纸本)9798400711367
CG (computer graphics) is a popular field of CS (computer Science), but many students find this topic difficult due to it requiring a large number of skills, such as mathematics, programming, geometric reasoning, and creativity. Over the past few years, researchers have investigated ways to harness the power of GenAI (Generative Artificial Intelligence) to improve teaching. In CS, much of the research has focused on introductory computing. A recent study evaluating the performance of an LLM (Large Language Model), GPT-4 (text-only), on CG questions, indicated poor performance and reliance on detailed descriptions of image content, which often required considerable insight from the user to return reasonable results. So far, no studies have investigated the abilities of LMMs (Large Multimodal Models), or multimodal LLMs, to solve CG questions and how these abilities can be used to improve teaching. In this study, we construct two datasets of CG questions requiring varying degrees of visual perception skills and geometric reasoning skills, and evaluate the current state-of-the-art LMM, GPT-4o, on the two datasets. We find that although GPT-4o exhibits great potential in solving questions with visual information independently, major limitations still exist to the accuracy and quality of the generated results. We propose several novel approaches for CG educators to incorporate GenAI into CG teaching despite these limitations. We hope that our guidelines further encourage learning and engagement in CG classrooms.
computer graphics and technologies have currently come a long way from engineering methods of environment and space parameter calculation. In the spheres of light engineering and lighting design, it is time to switch ...
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computer graphics and technologies have currently come a long way from engineering methods of environment and space parameter calculation. In the spheres of light engineering and lighting design, it is time to switch to modelling of illumination using mainly graphic lighting effects and imagery rather than digital values. Such transfer is impossible without trying to form the methodology and recommendations for 3D modelling of illumination. The authors of this article attempt to describe the key approaches to 3D modelling of museum illumination using contemporary software. The operating lighting installation of one of the halls of the Pushkin State Museum of Fine Arts is used as an example. Based on the achieved results of the research, we tried to describe the methodology and to provide recommendations for quality design and modelling of illumination in any exhibition spaces. The described methodology may be useful both for lighting engineers, architects, designers, and for curators and museum employees. The former may use the methodology for technical approach and implementation of museum illumination while the latter may use it to find a common language with the former and to compile more accurate terms of reference for them.
Grading computer graphics programming assessments and generating formative and summative feedback can require significant effort on the part of human experts. Since these assessments generate visual outputs that can b...
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ISBN:
(纸本)9798400705328
Grading computer graphics programming assessments and generating formative and summative feedback can require significant effort on the part of human experts. Since these assessments generate visual outputs that can be static or animated, determining correctness may be subjective. For feedback to be effective, it must be delivered in a timely manner. This can be a challenge for introductory computer graphics-based courses since cohort size can be substantial, errors in visual output can be subtle, and causes of errors are often not obvious. In this paper, we explore the feasibility of an automated system for marking visual output and providing program implementation feedback for learners in an introductory computer graphics-based design course in three short programming assessments, including static and animated scenes. To assess the effectiveness of our approach, we compare the marks generated by our tool with those assigned by a human expert. We show that it is possible to automate marking, providing both a grade based on the visual output and formative feedback on source code in the style of a human marker. This can improve objective consistency, grade reproducibility, and reduce marking time, enabling a course to scale to support large cohorts without the need for more resourcing for human markers. We describe lessons learnt and potential pitfalls to assist educators with introducing automated marking for their courses. Finally, we identify areas for future refinement and development of our automated system.
Online crowdsourcing platforms have made it increasingly easy to perform evaluations of algorithm outputs with survey questions like "which image is better, A or B?", leading to their proliferation in vision...
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Online crowdsourcing platforms have made it increasingly easy to perform evaluations of algorithm outputs with survey questions like "which image is better, A or B?", leading to their proliferation in vision and graphics research papers. Results of these studies are often used as quantitative evidence in support of a paper's contributions. On the one hand we argue that, when conducted hastily as an afterthought, such studies lead to an increase of uninformative, and, potentially, misleading conclusions. On the other hand, in these same communities, user research is underutilized in driving project direction and forecasting user needs and reception. We call for increased attention to both the design and reporting of user studies in computer vision and graphics papers towards (1) improved replicability and (2) improved project direction. Together with this call, we offer an overview of methodologies from user experience research (UXR), human-computer interaction (HCI), and applied perception to increase exposure to the available methodologies and best practices. We discuss foundational user research methods (e.g., needfinding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction. We provide further pointers to the literature for readers interested in exploring other UXR methodologies. Finally, we describe broader open issues and recommendations for the research community.
The articles in this special section focus on methodologies that have been or could be used to help fight pandemics with computer graphics (CG). The development of new algorithms and computational solutions to simulat...
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The articles in this special section focus on methodologies that have been or could be used to help fight pandemics with computer graphics (CG). The development of new algorithms and computational solutions to simulate, visualize, and predict scenarios in the pandemic world has been a reality in recent years. Many researchers at universities and companies pursued the objective of proposing alternatives that would help people in this challenging period. Many of these alternatives involve solutions that contain techniques such as image processing, visualization, animation, and interaction, in short, processing and generation of graphic data. All these proposed techniques and methodologies are of great value to society and must be recorded in an appropriate manner and for an appropriate period of time, so that they can continue to be used, applied, and improved.
In this research, we analyze the impact of the use of augmented reality (AR) during computer graphics course. First, modeling sessions were conducted using Unity and Vuforia, and linked to the course subjects. Then, a...
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ISBN:
(纸本)9798350366716;9798350366709
In this research, we analyze the impact of the use of augmented reality (AR) during computer graphics course. First, modeling sessions were conducted using Unity and Vuforia, and linked to the course subjects. Then, a questionnaire was applied to measure the motivation to the course and its future applications, the importance of augmented reality in the course and in their professional education. As a result, 64% of students considered AR is extremely or very important in the practical part, 61% of students considered AR inclusion in the teaching-learning process was extremely or very important, 70% of students considered the use of Vuforia is extremely or very beneficial in their education, 66% of students are extremely or very motivated about the use of computer graphics in AR and 70% of students are extremely or very motivated to develop current applications using. That indicates AR inclusion was important to get student attention and motivation.
The research and production of computer graphics imagery and animation at The Ohio State University started with the artistic work of Prof. Charles Csuri. He developed the computer graphics Research Group in response ...
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The research and production of computer graphics imagery and animation at The Ohio State University started with the artistic work of Prof. Charles Csuri. He developed the computer graphics Research Group in response to the award of a National Science Foundation Grant in 1974, and the group transferred its technology to a commercial production effort, Cranston/Csuri Productions, Inc., in 1981. CGRG evolved into the Advanced Computing Center for the Arts and Design in 1987. This article provides an historical review of the significant activities of these groups.
The research and production of computer graphics imagery and animation at The Ohio State University started with the artistic work of Prof. Charles Csuri. He developed the computer graphics Research Group in response ...
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The research and production of computer graphics imagery and animation at The Ohio State University started with the artistic work of Prof. Charles Csuri. He developed the computer graphics Research Group in response to the award of a National Science Foundation Grant in 1974, and the group transferred its technology to a commercial production effort, Cranston/Csuri Productions, Inc., in 1981. CGRG evolved into the Advanced Computing Center for the Arts and Design in 1987. This article provides an historical review of the significant activities of these groups.
Black holes are among the most extreme objects known to exist in nature. As such, they are excellent laboratories for testing fundamental theories and studying matter in conditions that cannot be found anywhere else i...
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Black holes are among the most extreme objects known to exist in nature. As such, they are excellent laboratories for testing fundamental theories and studying matter in conditions that cannot be found anywhere else in the Universe. In this article, we highlight the relevance of black holes in modern physical and astronomical research and present one of the possible paths to explain observations and probe physics with the aid of numerical simulations. We briefly review dynamical-spacetime general-relativistic magneto-hydrodynamic (GRMHD) calculations as fundamental tools to study the local properties of black holes and matter around them. Then, we discuss the need for general-relativistic radiation transport to propagate the local information about light obtained with GRMHD simulations to our telescopes. Finally, we present accretion onto binary black holes as a key area of study for testing general relativity and plasma physics. The goal of our article is to introduce the reader to some of the methods in current black hole research and to point out how improvements in hardware and software for computer graphics support advancements in the field.
This paper presents the reproduction of two studies focused on the perception of micro and macro expressions of Virtual Humans (VHs) generated by computer graphics (CG), first described in 2014 and replicated in 2021....
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ISBN:
(纸本)9798400716270
This paper presents the reproduction of two studies focused on the perception of micro and macro expressions of Virtual Humans (VHs) generated by computer graphics (CG), first described in 2014 and replicated in 2021. The 2014 study referred to a VH realistic, whereas, in 2021, it referred to a VH cartoon. In our work, we replicate the study by using a realistic CG character. Our main goals are to compare the perceptions of micro and macro expressions between levels of realism (2021 cartoon versus 2023 realistic) and between realistic characters in different periods (i.e., 2014 versus 2023). In one of our results, people more easily recognized micro expressions in realistic VHs than in a cartoon VH. In another result, we show that the participants' perception was similar for both micro and macro expressions in 2014 and 2023.
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