材料虚构:通过物质感知来比较基于物理的染和生成AI图像
Yuguang Zhao1,2,3, Jeroen Stumpel4,5, Huib de Ridder1,6,7
1Perceptual Intelligence Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, the Netherlands.
Journal of vision
|March 16, 2026
概括
生成的人工智能图像可以探索物质感知,形成一个新的视觉媒介. 尽管有初始的差异,但像稳定扩散这样的AI模型,当被限制时,会创造与传统方法相似的感知空间.
科学领域:
- 计算机视觉 计算机视觉
- 人与计算机的交互
- 感知心理学 感知心理学
背景情况:
- 生成型人工智能 (AI) 模型正在创建新的图像类型.
- 这些AI图像可以在视觉上令人信服,但在物理上不合理.
- 研究人工智能图像在视觉感知和作为新媒介的作用至关重要.
研究的目的:
- 为了确定生成人工智能图像是否与中等独立的感知空间保持一致.
- 将人工智能生成的图像与基于物理的染 (PBR) 的人类相似性判断进行比较.
- 评估AI图像生成在不同模型和约束条件中的结构一致性.
主要方法:
- 人类相似性判断被用来比较来自三个AI模型 (DALL-E 2,Midjourney v2,稳定扩散v1.5) 与BRDF数据集 (MERL) 的图像.
- 实验1:人工智能模型从32种材料的文本提示生成图像.
- 实验2:稳定扩散使用相同文本提示的深度地图约束.
主要成果:
- 最初的AI模型 (DALL-E 2,Midjourney v2) 与1D MERL空间相比,产生了无关的2D感知空间.
- 使用深度图限制的稳定扩散产生了强大的,非常相似的二维感知空间.
- 这些受约束的AI空间与MERL和其他物质感知研究的结构相似.
结论:
- 生成的人工智能图像,特别是当被限制时,可以形成探索物质感知的新媒介.
- 人工智能模型展示了创造感知相关图像的潜力.
- 进一步的研究可以利用人工智能来理解不同图像模式的视觉感知.
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