Generative AI in Color-Changing Systems: Re-Programmable 3D Object Textures with Material and Design Constraints

TL;DR

This paper explores integrating generative AI with photochromic materials to enable automated, constraint-aware design of reprogrammable 3D surface textures, facilitating rapid and viable pattern creation.

Contribution

It proposes a framework for constraining generative AI to physical material limitations, enhancing design efficiency for reprogrammable photochromic surfaces.

Findings

Identifies current limitations of AI in creating viable photochromic textures.

Suggests approaches to incorporate material constraints into AI generative processes.

Highlights potential for rapid, automated pattern exploration on reprogrammable surfaces.

Abstract

Advances in Generative AI tools have allowed designers to manipulate existing 3D models using text or image-based prompts, enabling creators to explore different design goals. Photochromic color-changing systems, on the other hand, allow for the reprogramming of surface texture of 3D models, enabling easy customization of physical objects and opening up the possibility of using object surfaces for data display. However, existing photochromic systems require the user to manually design the desired texture, inspect the simulation of the pattern on the object, and verify the efficacy of the generated pattern. These manual design, inspection, and verification steps prevent the user from efficiently exploring the design space of possible patterns. Thus, by designing an automated workflow desired for an end-to-end texture application process, we can allow rapid iteration on different practicable patterns. In this workshop paper, we discuss the possibilities of extending generative AI systems, with material and design constraints for reprogrammable surfaces with photochromic materials. By constraining generative AI systems to colors and materials possible to be physically realized with photochromic dyes, we can create tools that would allow users to explore different viable patterns, with text and image-based prompts. We identify two focus areas in this topic: photochromic material constraints and design constraints for data-encoded textures. We highlight the current limitations of using generative AI tools to create viable textures using photochromic material. Finally, we present possible approaches to augment generative AI methods to take into account the photochromic material constraints, allowing for the creation of viable photochromic textures rapidly and easily.