Synthesizing the Four Seasons of a Scene from Large-Scale Web Images

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.