Like a semester long graduate seminar on Optimization in computergraphics and interactivetechniques, this course looks at Optimization through the lens of 13 technical papers selected by the lecturers. The lecturers...
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Industry panelists share perspectives and insights for students, educators, and creative professionals who are considering careers in animation, computergraphics, creative technologies, and interactivetechniques. Wi...
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Industry panelists share perspectives and insights for students and educators who are considering careers in animation, computergraphics, and interactivetechniques. Creative industries continue to transform as a res...
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Industry panelists share perspectives and insights for students and educators who are considering careers in computergraphics and interactivetechniques. Creative industries have transformed as a result of the global...
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he computer Science Curricula 2023 (CS2023) was endorsed by their sponsoring organizations: the Association of Computing Machinery (acm), IEEE Computing Society (IEEE CS), and the Association for the Advancement of Ar...
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ISBN:
(纸本)9798400711367
he computer Science Curricula 2023 (CS2023) was endorsed by their sponsoring organizations: the Association of Computing Machinery (acm), IEEE Computing Society (IEEE CS), and the Association for the Advancement of Artificial Intelligence (AAAI) in early 2024. The document was produced over the course of four years and was the collaborative work of approximately 100 volunteers from six continents. In keeping with current curricular design principles, the guidelines incorporate a competency model (what is learned) in addition to the traditional knowledge model (what is taught). This talk is geared to anyone interested in computer science graphics education who would like to learn more about the guidelines and the on-going computing curricular efforts. We will describe the organization of CS2023, the graphics and interactivetechniques Knowledge Area including its new Knowledge Unit, Society Ethics and the Profession.
This essay explores the robotic art installation 'Sisyphus,' interpreting its cyclical confrontation as a symbolic depiction of the ceaseless struggle between systemic power and collective resistance. By analy...
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This essay explores the robotic art installation 'Sisyphus,' interpreting its cyclical confrontation as a symbolic depiction of the ceaseless struggle between systemic power and collective resistance. By analyzing the dynamic reiteration of construction and demolition, we explore the concepts of futility, resistance, and the continual process of socio-political negotiation. Further, we investigate parallels between the installation's mechanical operations and the socio-political interactions. Ultimately, we argue that 'Sisyphus' portrays not a futile endeavor but resilience, ceaseless evolution, and the potential of resistance as transformative power.
In the domain of computergraphics, achieving high visual quality in real-time rendering remains a formidable challenge due to the inherent time-quality tradeoff. Conventional real-time rendering engines sacrifice vis...
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In the domain of computergraphics, achieving high visual quality in real-time rendering remains a formidable challenge due to the inherent time-quality tradeoff. Conventional real-time rendering engines sacrifice visual fidelity for interactive performance, while image generation using path-tracing techniques can be exceedingly time-consuming. In this article, we introduce RenderGAN, a deep learning-based solution designed to address this critical challenge in real-time rendering. RenderGAN uses G-Buffers and information from a real-time rendering engine as inputs to produce output images with exceptional visual fidelity. Its encoder-decoder architecture, trained using the Generative Adversarial Network (GAN) framework with perceptual loss, enhances image realism. To evaluate RenderGAN's effectiveness, we quantitatively compare the generated images with those of a path-tracing engine, obtaining a remarkable Universal Image Quality Index (UIQI) value of 0.898. RenderGAN's open source nature fosters collaboration, driving advancements in real-time computergraphics and rendering techniques. By bridging the gap between real-time and path-tracing rendering, RenderGAN opens new horizons for accelerated image generation, inspiring innovation and unlocking the full potential of real-time visual experiences. Project page: https://***/marcomameli1992/RenderNet
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