Image-Based Models for Specularity Propagation in Diminished Reality.

The aim of Diminished Reality (DR) is to remove a target object in a live video stream seamlessly. In our approach, the area of the target object is replaced with new texture that blends with the rest of the image. The result is then propagated to the next frames of the video. One of the important stages of this technique is to update the target region with respect to the illumination change. This is a complex and recurrent problem when the viewpoint changes. We show that the state-of-the-art in DR fails in solving this problem, even under simple scenarios. We then use local illumination models to address this problem. According to these models, the variation in illumination only affects the specular component of the image. In the context of DR, the problem is therefore solved by propagating the specularities in the target area. We list a set of structural properties of specularities which we incorporate in two new models for specularity propagation. Our first model includes the same property as the previous approaches, which is the smoothness of illumination variation, but has a different estimation method based on the Thin-Plate Spline. Our second model incorporates more properties of the specularity's shape on planar surfaces. Experimental results on synthetic and real data show that our strategy substantially improves the rendering quality compared to the state-of-the-art in DR.