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Generating Procedural Materials From Text or Image Prompts

DescriptionNode graph systems are ubiquitous for material design in computer graphics. They allow the use of visual programming to achieve desired effects without writing code. We propose a multi-modal node graph generation neural architecture for high-quality procedural material synthesis which can be conditioned on different inputs (text or image prompts).

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