NEURAL RENDERING SYSTEMS TO PRODUCE HYPER-REALISTIC ARTISTIC VISUALS FOR MULTIMEDIA PRODUCTIONS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7508Keywords:
Neural Rendering, Neural Radiance Fields (NERF), Deep Generative Models, Volumetric Rendering, Multimedia Visual Production, Photorealistic Image SynthesisAbstract [English]
Neural rendering has been the disruptive technology in the creation of very realistic content of the visual multimedia production that the computer graphics and deep learning have substituted. This paper examines the neural rendering systems that can be trained to produce hyper-realistic artistic images through the acquisition of the complex representations of scenes based on multi-view image representations. The suggested architecture compiles the neural radiance field modeling, deep neural networks as well as volumetric rendering to reproduce detailed three-dimensional scenes as well as produce photorealistic images in new perspectives. Multi-view data acquisition, neural feature encoding, and radiance field estimation are the system architecture elements based on deep learning models that capture geometry, lighting, texture and color interaction within a scene. Experimental analysis of neural rendering methods has shown that they render visual fidelity, geometric consistency and rendering realism by a wide margin than the standard computer graphics pipelines. The quantitative investigation of the measures of the quality of rendering, such as the similarity index of the structure, the perceptual realism scores, and the reconstruction accuracy, reveals the significant progress of visual detail and scene modeling.
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Copyright (c) 2026 Mandeep Kaur, Dr. Mercy Paul Selvan, Barkha Bhardwaj, Simranjeet Nanda, Shanthi P, Ashutosh Kulkarni, Prasanna Kumar E
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