PlantoGraphy: Incorporating Iterative Design Process into Generative Artificial Intelligence for Landscape Rendering

Abstract

Landscape renderings are realistic images of landscape sites, allowing stakeholders to perceive better and evaluate design ideas. While recent advances in Generative Artificial Intelligence (GAI) enable automated generation of landscape renderings, the end-to-end methods are not compatible with common design processes, leading to insufficient alignment with design idealizations and limited cohesion of iterative landscape design. Informed by a formative study for comprehending design requirements, we present PlantoGraphy, an iterative design system that allows for interactive configuration of GAI models to accommodate human-centered design practice. A two-stage pipeline is incorporated: first, concretization module transforms conceptual ideas into concrete scene layouts with a domain-oriented large language model; and second, illustration module converts scene layouts into realistic landscape renderings using a fine-tuned low-rank adaptation diffusion model. PlantoGraphy has undergone a series of performance evaluations and user studies, demonstrating its effectiveness in landscape rendering generation and the high recognition of its interactive functionality.

arXiv comment27 pages, 14 figures, The ACM CHI 2024 conference

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Cited by in corpus (4)

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• GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design
• KinemaFX: A Kinematic-Driven Interactive System for Particle Effect Exploration and Customization