Controllable Procedural Generation of Landscapes

Landscapes, recognized for their indispensable role in large-scale scenes, are experiencing growing demand. However, the manual modeling of such content is labor-intensive and lacks efficiency. Procedural Content Generation (PCG) techniques enable the rapid generation of diverse landscape elements. Nevertheless, ordinary users may encounter difficulties controlling these methods for desired results. In this paper, we introduce a controllable framework for procedurally generating landscapes. We integrate state-of-the-art Large Language Models (LLMs) to enhance user accessibility and control. By converting plain text inputs into parameters through LLMs, our framework allows ordinary users to generate a batch of plausible landscapes tailored to their specifications. A parameter-controlled PCG procedure is designed to leverage optimization techniques and employ rule-based refinements. It achieves harmonious layering in terrains, zoning, and roads while enabling aesthetic arrangement of vegetation and artificial elements. Extensive experiments demonstrate our framework's effectiveness in generating landscapes comparable to those crafted by experienced architects. Our framework has the potential to enhance the productivity of landscape designers significantly.