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Hyper-Realist Rendering: A Theoretical Framework

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arxiv 2401.12853 v1 pith:MHBRBYHN submitted 2024-01-23 cs.GR

classification cs.GR
keywords hyper-realistobtainrealityillusionsrenderingrepresentationalvisualacceptable
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This is the first paper in a series on hyper-realist rendering. In this paper, we introduce the concept of hyper-realist rendering and present a theoretical framework to obtain hyper-realist images. We are using the term Hyper-realism as an umbrella word that captures all types of visual artifacts that can evoke an impression of reality. The hyper-realist artifacts are visual representations that are not necessarily created by following logical and physical principles and can still be perceived as representations of reality. This idea stems from the principles of representational arts, which attain visually acceptable renderings of scenes without implementing strict physical laws of optics and materials. The objective of this work is to demonstrate that it is possible to obtain visually acceptable illusions of reality by employing such artistic approaches. With representational art methods, we can even obtain an alternate illusion of reality that looks more real even when it is not real. This paper demonstrates that it is common to create illusions of reality in visual arts with examples of paintings by representational artists. We propose an approach to obtain expressive local and global illuminations to obtain these stylistic illusions with a set of well-defined and formal methods.

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Cited by 3 Pith papers

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    cs.CV 2024-11 conditional novelty 3.0 of 10

    Two minimal convolutional networks match big pretrained models on an easy fake-face dataset and train far faster, but they fail on a harder 140k face dataset.

  2. Mul2MAR: A Multi-Marker Mobile Augmented Reality Application for Improved Visual Perception

    cs.GR 2025-02 reject novelty 2.0 of 10

    Mul2MAR combines ARToolKit markers, OpenGL rendering, and red-cyan anaglyph glasses to show virtual objects in apparent 3D on a mobile device, but gives no quantitative validation beyond the author's prior work.

  3. Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey

    cs.AI 2024-12 conditional novelty 2.0 of 10

    A survey reviewing how geometric features (bounding boxes, keypoints, poses, 3D representations) are used in AI for extracting, analyzing, and synthesizing artistic images, concluding that geometry improves performanc...

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