通过令牌扭曲进行零拍摄视频翻译
IEEE transactions on visualization and computer graphics
|November 26, 2025
概括
TokenWarping通过使用光学流来扭曲查询,键和值补丁来增强生成AI视频翻译. 这种新的框架可以提高时间连贯性和视觉质量,而不需要额外的培训.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 生成型模型 生成型模型
背景情况:
- 生成型人工智能已经推进了视频任务,但与图像模型相比,当前的模型缺乏视觉质量和用户控制.
- 现有的基于扩散的视频编辑方法由于依赖特定的注意力补丁,难以保留本地结构.
研究的目的:
- 介绍TokenWarping,这是一个用于暂时连贯的视频翻译的新框架.
- 通过结合查询补丁信息来解决当前视频编辑模型中的限制.
主要方法:
- 从源视频中提取光学流.
- 在扩散模型无声化过程中使用光学流来扭曲前一的查询,键和值补丁.
- 在中对齐补丁,以增强特征聚合和时间一致性.
主要成果:
- TokenWarping通过查询补丁曲线改进了功能聚合.
- 通过扭曲键和值补丁来确保时间一致性.
- 与最先进的方法相比,表现出优越的质量和数量性能.
结论:
- TokenWarping提供了一种新的方法来实现时间连贯的视频翻译.
- 该框架与现有方法无集成,不需要额外的培训.
- 在视频翻译任务中取得最先进的结果.
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