用几何信息提升AI对艺术图像的分析与生成能力
Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey
- 将几何数据融入AI模型，增强对艺术图像的表征能力
- 改善风格与内容分离效果，提升生成图像质量
- 适合关注艺术生成、图像理解的AI研究者
人工智能显著提升了数字艺术图像的分析、识别与生成能力。本综述强调将几何数据融入AI模型带来的巨大优势，解决类别间差异大、领域差异明显以及风格与内容分离困难等问题。通过引入几何信息，模型不仅提升了生成图像的质量，还更有效地实现风格与内容解耦，利用模型固有偏差和共享数据特征。本文探讨了从艺术图像中提取几何数据的方法、其对人类感知的影响，以及在判别任务中的应用。同时讨论了创新标注技术对数据质量的提升作用，以及几何数据在增强模型适应性与输出精细化方面的潜力。总体而言，几何引导显著提升模型在分类与生成任务中的表现，为未来视觉艺术领域AI应用提供关键洞见。
原文摘要 · Abstract (English)
Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challenges such as high inter-class variations, domain gaps, and the separation of style from content by incorporating geometric information. Models not only improve AI-generated graphics synthesis quality, but also effectively distinguish between style and content, utilizing inherent model biases and shared data traits. We explore methods like geometric data extraction from artistic images, the impact on human perception, and its use in discriminative tasks. The review also discusses the potential for improving data quality through innovative annotation techniques and the use of geometric data to enhance model adaptability and output refinement. Overall, incorporating geometric guidance boosts model performance in classification and synthesis tasks, providing crucial insights for future AI applications in the visual arts domain.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。