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NADM:景观绘画视频生成噪声感知扩散模型
IEEE transactions on cybernetics
|June 24, 2025
概括
这项研究引入了一个新的数据集和一个噪声感知扩散模型 (NADM),用于生成动态景观绘画视频. 该方法通过捕捉美学活力和平稳的过渡来增强艺术视频生成.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 数字艺术 数字艺术 数字艺术
背景情况:
- 传统的风景画是静态的,限制了观众对动态场景的想象力.
- 新兴的文本到视频 (T2V) 模型产生自然视频,但与艺术美学和特定数据集作斗争.
- 生成高质量,动态的风景绘画视频,由于风格复杂性和数据稀缺性,提出了独特的挑战.
研究的目的:
- 开发一个新的文本到视频 (T2V) 数据集,专门用于景观绘画视频.
- 提出一种新的T2V模型,即噪声感知扩散模型 (NADM),用于生成动态和美观的景观绘画视频.
- 解决当前T2V方法在捕捉艺术视频的动态美学方面的局限性.
主要方法:
- 介绍了景观绘画视频高清 (LPV-HD) 数据集.
- 开发了基于稳定扩散的噪声感知扩散模型 (NADM).
- 实现了一种带有双重注意力机制的运动模块,用于动态图像转换.
- 使用无监督对比学习的噪声适配器来增强潜在空间的美丽.
- 采用光流来进行间波,以提高视频流性.
主要成果:
- 拟议的NADM成功生成了动态景观绘画视频,保留了原始艺术品的精髓.
- 双重注意力机制有效地捕捉到景观图像中的动态转变.
- 噪音适配器和插入有助于整体美学质量和视频流性.
- LPV-HD数据集为艺术视频生成的未来研究提供了宝贵的资源.
结论:
- NADM和LPV-HD数据集在生成动态艺术视频方面取得了重大进展.
- 该方法成功地平衡了艺术本质的保存与动态视觉体验的创造.
- 这项工作为数字艺术创作和传统艺术作品的动画开辟了新的可能性.
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