Mesh-guided optimized retexturing for image and video
Publication in refereed journal


摘要This paper presents a novel approach for replacing textures of specified regions in the input image and video using stretch-based mesh optimization. The retexturing results have the similar distortion and shading effect conforming to the unknown underlying geometry and lighting conditions. For replacing textures in a single image, two important steps are developed: The stretch-based mesh parameterization incorporating the recovered normal information is deduced to imitate perspective distortion of the region of interest; the Poisson-based refinement process is exploited to account for texture distortion at fine scale. The luminance of the input image is preserved through color transfer in YCbCr color space. Our approach is independent of the replaced textures. Once the input image is processed, any new textures can be applied to efficiently generate the retexturing results. For video retexturing, we propose key-frame-based texture replacement extended and generalized from the image retexturing. Our approach repeatedly propagates the replacement results of key frames to the rest of the frames. We develop the local motion optimization scheme to deal with the inaccuracies and errors of robust optical flow when tracking moving objects. Visibility shifting and texture drifting are effectively alleviated using graphcut segmentation algorithm and the global optimization to smooth trajectories of the tracked points over temporal domain. Our experimental results showed that the proposed approach can generate visually pleasing results for retextured images and video.
著者Guo YW, Sun HQ, Peng QS, Jiang ZD
期刊名稱IEEE Transactions on Visualization and Computer Graphics
頁次426 - 439
關鍵詞graphcut segmentation; parameterization; Poisson equation; texture replacement
Web of Science 學科類別Computer Science; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING

上次更新時間 2021-18-02 於 23:15