Visualizing the Lifespan of Industrial Objects with AI-Generated Texture Space
摘要
Real-world industrial assets exhibit diverse surface appearances due to multiple factors such as rust, wear, and dirt accumulation. However, simulation environments contain identical or clean object appearances, causing a significant gap between synthetic and real data. Moreover, manually designing unique and intricate realistic textures is inefficient. In this paper, we present a scalable texture synthesis pipeline to produce a multidimensional texture space featuring various industrial object lifespans. It consists of three stages: (1) state translation: to map textures to different states, (2) state interpolation: to generate smooth transitions across different statuses, and (3) space blending: to combine multiple factors affecting object appearances. We evaluate our pipeline with three common industrial assets: dollies, small load carrier boxes, and pallets. As a result, our pipeline generalizes to new shape variants, while preserving realistic and smooth transitions in extreme factors.