用生成模型设计手工织物图案，解决艺术风格生成难题。
Handloom Design Generation Using Generative Networks
- 结合生成模型与风格迁移，实现织物图案自动生成
- 用户评分验证生成效果，证明方法有效
- 开源新数据集NeuralLoom，推动领域研究
本文提出基于深度学习的服装设计生成技术，聚焦手工织物图案，探讨相关挑战与应用前景。当前生成神经网络在理解艺术设计与合成方面仍不充分。本研究采用多种前沿生成模型与风格迁移算法，评估其在该任务中的表现，并通过用户评分进行结果验证。同时，本文发布了一个新的数据集NeuralLoom，用于支持设计生成研究。
原文摘要 · Abstract (English)
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.
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