ChatPattern: Layout Pattern Customization via Natural Language
Zixiao Wang Yunheng Shen Xufeng Yao Wenqian Zhao Yang Bai Farzan Farnia Bei Yu
Existing works focus on fixed-size layout pattern generation, while the more
practical free-size pattern generation receives limited attention. In this
paper, we propose ChatPattern, a novel Large-Language-Model (LLM) powered
framework for flexible pattern customization. ChatPattern utilizes a two-part
system featuring an expert LLM agent and a highly controllable layout pattern
generator. The LLM agent can interpret natural language requirements and
operate design tools to meet specified needs, while the generator excels in
conditional layout generation, pattern modification, and memory-friendly
patterns extension. Experiments on challenging pattern generation setting shows
the ability of ChatPattern to synthesize high-quality large-scale patterns.