Collaborative garment design through group chatting with generative industrial large models

Abstract

The collaborative garment designing lifecycle involves stages such as designing, styling, and patterning. Some of these stages can be partially or fully automated using industrial large models (LMs), such as generative and large language models. The key to quick and cost-effective order fulfillment is the orchestration of group interactions, or a group chat, between the stakeholders and LMs in garment design. However, certain unaddressed aspects, such as knowledge retention, generalization, and complexity of group interaction, are critical to realizing group chat for garment design. This study proposes a framework called ChatFashion for group chat in garment design. Transformer, a core construct of the proposed framework, orchestrates interaction among stakeholders and industrial LMs. It undergoes an evolution with the intelligence it picks up from its interaction with diverse stakeholders and industrial LMs, allowing it to act as a one-stop solution for multidisciplinary design needs. This study contributes to theory in the following aspects. First, it proposes transformers to eliminate concerns about knowledge retention by industrial LMs. Second, while other studies focus on the benefits of industrial LMs to simplify individual stages in garment design, this study introduces the design and demonstration of a ChatFashion framework for collaborative garment designing using industrial LMs. Finally, this study advances the literature on prompt engineering of industrial LMs by utilizing collaborative learning (or models learning from each other) to capture and orchestrate the group chat among stakeholders, signifying its practicality and value for research in garment design.