用图像块聚类让静态图动起来，不复制原图，而是重新诠释。
Recomposed realities: animating still images via patch clustering and randomness
- 通过聚类提取图像块，再随机采样重建新图像
- 能生成与原图概念不同但局部结构相似的动态画面
- 适合做创意动画或跨域视觉重构
我们提出一种基于图像块的重构与动画方法，利用已有图像数据使静态图像产生动态效果。从精选数据集中提取图像块，使用k-means聚类进行分组，并通过匹配与随机采样重建目标图像。该方法强调对图像的重新诠释而非简单复制，允许源图像与目标图像在概念上存在差异，同时共享局部结构特征。
原文摘要 · Abstract (English)
We present a patch-based image reconstruction and animation method that uses existing image data to bring still images to life through motion. Image patches from curated datasets are grouped using k-means clustering and a new target image is reconstructed by matching and randomly sampling from these clusters. This approach emphasizes reinterpretation over replication, allowing the source and target domains to differ conceptually while sharing local structures.
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。