【摘要】 针对传统的纹理合成与颜色迁移对自然场景的四季合成问题存在的失效问题,提出了基于大规模数据驱动的方法.首先,从互联网上获取关于场景地点的图像并且通过全局匹配性与局部一致性约束进行粗略筛选;其次,利用动态规划算法简化场景图像特征点匹配,并通过最小化约束函数确定场景参考帧以及相应场景帧;最后,将相应场景帧通过变换对齐到参考帧,并根据梯度域方法补全对齐场景从而形成场景四季序列.实验结果表明,该方法能够生成更加合理的关于场景的四季图像以及四季变化鲜明的延时摄影视频.更多还原

【Abstract】 Traditional color or texture transfer methods are limited for synthesizing the four-season series of a scene, or transferring a scene to a different season due to the complexity of seasonal evolution. In this paper, we propose a data-driven approach, and heavily relies on large number of images on the web. We embed carefully mined and aligned images crawled from the web into a time series of the scene, and let the manifold of complex scene-specific seasonal evolution unfold itself from the data. Because we reconstruct the time series of the scene over an entire year, we get a loopable timelapse video for free and can extract a four-season series out of it. Further on, we use the result to create novel four-season mosaicking that renders interesting visual effect.更多还原