Contour-Based Registration and Retexturing of Cartoon-Like Videos

Retexturing videos of deformable surfaces is an important problem in computer vision as it has a wide variety of applications. A key step in producing visually pleasing retexturing results is registration. Traditional registration methods require a certain amount of texture on the surface in order to capture all the deformation details. However, in cases such as cartoon videos, there is a high number of smooth contours and only little or spurious texture. We propose a novel method for registering and retexturing cartoon-like videos by means of joint contour detection and point to point curve matching. The main idea is to fit a parametric 3D active surface model in the spatiotemporal space, utilizing a regularization term which limits the change in curvature over time. We show that with cross-validation it is possible to automatically estimate a suitable value for the regularization parameter, controlling the tradeoff between the regularization and the data term. We report convincing registration and retexturing results on cartoon videos.