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首页> 外文期刊>IEEE transactions on visualization and computer graphics >RotoTexture: Automated Tools for Texturing Raw Video
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RotoTexture: Automated Tools for Texturing Raw Video

机译：RotoTexture：自动处理原始视频的工具

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摘要

We propose a video editing system that allows a user to apply a time-coherent texture to a surface depicted in the raw video from a single uncalibrated camera, including the surface texture mapping of a texture image and the surface texture synthesis from a texture swatch. Our system avoids the construction of a 3D shape model and instead uses the recovered normal field to deform the texture so that it plausibly adheres to the undulations of the depicted surface. The texture mapping method uses the nonlinear least-squares optimization of a spring model to control the behavior of the texture image as it is deformed to match the evolving normal field through the video. The texture synthesis method uses a coarse optical flow to advect clusters of pixels corresponding to patches of similarly oriented surface points. These clusters are organized into a minimum advection tree to account for the dynamic visibility of clusters. We take a rather crude approach to normal recovering and optical flow estimation, yet the results are robust and plausible for nearly diffuse surfaces such as faces and t-shirts
机译：我们提出了一种视频编辑系统，该系统允许用户将时间相关纹理应用于来自单个未经校准的摄像机的原始视频中描绘的表面，包括纹理图像的表面纹理映射和纹理样本的表面纹理合成。我们的系统避免了3D形状模型的构建，而是使用恢复的法向场使纹理变形，以使纹理合理地粘附到所描绘表面的起伏。纹理映射方法使用弹簧模型的非线性最小二乘法优化来控制纹理图像的行为，因为该图像变形以匹配视频中不断变化的法向场。纹理合成方法使用粗糙的光流平移对应于相似取向的表面点的面片的像素簇。这些群集被组织到最小对流树中，以说明群集的动态可见性。对于正常的恢复和光流估计，我们采用了一种相当粗糙的方法，但是对于像脸和T恤衫这样的几乎扩散的表面，结果是可靠且合理的

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