Generative 3D appearance design: A survey of generation, segmentation and editing by artificial intelligence

Abstract The demand for appearance diversity in concept development often results in repetitive, labour-intensive tasks. Generative artificial intelligence (AI) offers a promising solution for automating and enhancing appearance design. Effective integration generative AI into appearance design workflows requires capabilities in generation, segmentation, and editing of 3D appearance elements. This paper systematically surveys generative AI technologies within the appearance design stage, explicitly focusing on three core domains: generation, segmentation, and editing. Recent generative techniques rapidly visualize 3D appearance concepts from textual inputs, minimizing manual effort. Segmentation techniques separate appearance components within generated models, enabling targeted refinements and selective modifications. Editing methods support detailed geometry, texture, colour, and attribute adjustments of existing concepts, facilitating iterative design refinement. While generative AI effectively automates initial ideation and simplifies complex modifications, seamless integration among generation, segmentation, and editing processes remains challenging. Future research should focus on unified frameworks and computational efficiency improvements. By surveying these core areas, this paper emphasizes generative AI’s potential to support or partially replace traditional manual appearance tasks, thereby enhancing creativity and efficiency.