Abstract
This paper proposes deep learning techniques of generating designs for clothing, focused on handloom fabric and discusses the associated challenges along with its application. The capability of generative neural network models in understanding artistic designs and synthesizing those is not yet explored well. In this work, multiple methods are employed incorporating the current state of the art generative models and style transfer algorithms to study and observe their performance for the task. The results are then evaluated through user score. This work also provides a new dataset NeuralLoom for the task of the design generation.
Abstract (translated)
本文提出了一种用于生成服装设计的深度学习技术，重点是手织布料，并讨论了相关的挑战及其应用。生成式神经网络模型在理解和合成艺术设计方面的能力尚未得到充分探索。在这项工作中，采用了多种方法，结合当前最先进的生成模型和风格转换算法来研究并观察它们在此任务中的性能表现。通过用户评分对结果进行评估。此外，本文还提供了一个新的数据集 NeuralLoom 用于设计生成任务。
URL
https://arxiv.org/abs/2505.14330
