现代生成模型复制艺术风格能力的批判性评估
A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Styles
AI总结：
本文批判性评估当代生成模型复制艺术风格的能力，基于新构建的AI-pastiche数据集和用户调查，从技术、美学等多维度分析其优势与局限，并探讨模型改进与创意应用机遇。
摘要：
近年来，生成式人工智能的进步催生了能够模仿多种艺术风格的复杂工具，为数字创意和艺术表达开辟了新的可能性。本文对当代生成模型的风格复制能力进行了批判性评估，从多个维度评价其优势与局限。我们考察了这些模型在生成图像时，在保持结构完整性和构图平衡的同时，有效再现传统艺术风格的程度。\n 该分析基于一个新的大型数据集，其中包含模仿过去艺术风格的人工智能生成作品，该数据集具有广泛的应用潜力，即“AI-pastiche”数据集。\n 本研究得到了广泛的用户调查支持，收集了关于该数据集的多样意见，并探讨了技术和美学方面的挑战，包括生成逼真且视觉上令人信服的输出的能力、模型处理广泛艺术风格的多功能性，以及它们在多大程度上遵循提示中概述的内容和风格规范。\n 本文旨在全面概述生成工具在风格复制方面的现状，提供对其技术和艺术局限的见解、模型设计和训练方法上的潜在改进，以及增强数字艺术、人机协作和更广泛创意领域的新兴机遇。
英文摘要：
In recent years, advancements in generative artificial intelligence have led to the development of sophisticated tools capable of mimicking diverse artistic styles, opening new possibilities for digital creativity and artistic expression. This paper presents a critical assessment of the style replication capabilities of contemporary generative models, evaluating their strengths and limitations across multiple dimensions. We examine how effectively these models reproduce traditional artistic styles while maintaining structural integrity and compositional balance in the generated images. The analysis is based on a new large dataset of AI-generated works imitating artistic styles of the past, holding potential for a wide range of applications: the "AI-pastiche" dataset. The study is supported by extensive user surveys, collecting diverse opinions on the dataset and investigation both technical and aesthetic challenges, including the ability to generate outputs that are realistic and visually convincing, the versatility of models in handling a wide range of artistic styles, and the extent to which they adhere to the content and stylistic specifications outlined in prompts. This paper aims to provide a comprehensive overview of the current state of generative tools in style replication, offering insights into their technical and artistic limitations, potential advancements in model design and training methodologies, and emerging opportunities for enhancing digital artistry, human-AI collaboration, and the broader creative landscape.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
