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Generative AI images can explore material perception, forming a new visual medium. Despite initial differences, AI models like Stable Diffusion, when constrained, create perceptually similar spaces to traditional methods.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Perceptual Psychology

Background:

  • Generative artificial intelligence (AI) models are creating novel image types.
  • These AI images can be visually convincing but not physically plausible.
  • Investigating AI images' role in visual perception and as a new medium is crucial.

Purpose of the Study:

  • To determine if generative AI images align with a medium-independent perceptual space.
  • To compare human similarity judgments of AI-generated images with physically based renderings (PBR).
  • To assess the structural consistency of AI image generation across different models and constraints.

Main Methods:

  • Human similarity judgments were used to compare images from three AI models (DALL-E 2, Midjourney v2, Stable Diffusion v1.5) against a BRDF dataset (MERL).
  • Experiment 1: AI models generated images from text prompts of 32 materials.
  • Experiment 2: Stable Diffusion used depth-map constraints with the same text prompts.

Main Results:

  • Initial AI models (DALL-E 2, Midjourney v2) produced unrelated 2D perceptual spaces compared to the 1D MERL space.
  • Stable Diffusion with depth-map constraints generated robust, highly similar 2D perceptual spaces.
  • These constrained AI spaces showed structural similarity to MERL and other material perception studies.

Conclusions:

  • Generative AI images, particularly when constrained, can form a new medium for exploring material perception.
  • AI models demonstrate potential for creating perceptually relevant imagery.
  • Further research can leverage AI for understanding visual perception across different image modalities.