Annoying shaky motion is one of the significant problems in home videos

This paper proposes a new approach for video stabilization. Most existing video stabilization methods adopt a framework of three steps, motion estimation, motion compensation and image composition. Camera motion is often estimated based on pairwise registration between frames. Thus, these methods often assume static scenes or distant backgrounds. Furthermore, for scenes with moving objects, robust methods are required for finding the dominant motion. Such assumptions and judgements could lead to errors in motion parameters. Errors are compounded by motion compensation which smoothes motion parameters. This paper proposes a method to directly stabilize a video without explicitly estimating camera motion, thus assuming neither motion models nor dominant motion. The method first extracts robust feature trajectories from the input video. Optimization is then performed to find a set of transformations to smooth out these trajectories and stabilize the video. In addition, the optimization also considers quality of the stabilized video and selects a video with not only smooth camera motion but also less unfilled area after stabilization. Experiments show that our method can deal with complicated videos containing near, large and multiple moving objects.

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This paper addresses the problem of real-time rendering for objects with complex materials under varying allfrequency illumination and changing view. Our approach extends the triple product algorithm by using local-frame parameterization, spherical wavelets, per-pixel shading and visibility textures. Storing BRDFs with local-frame parameterization allows us to handle complex BRDFs and incorporate bump mapping more easily. In addition, it greatly reduces the data size compared to storing BRDFs with respect to the global frame. The use of spherical wavelets avoids uneven sampling and energy normalization of cubical parameterization. Finally, we use per-pixel shading and visibility textures to remove the need for fine tessellations of meshes and shift most computation from vertex shaders to more powerful pixel shaders. The resulting system can render scenes with realistic shadow effects, complex BRDFs, bump mapping and spatially-varying BRDFs under varying complex illumination and changing view at real-time frame rates on modern graphics hardware.

This paper proposes scene warping, a layer-based stereoscopic image resizing method using image warping. The proposed method decomposes the input stereoscopic image pair into layers according to the depth and color information. A quad mesh is placed onto each layer to guide the image warping for resizing. The warped layers are composited by their depth orders to synthesize the resized stereoscopic image. We formulate an energy function to guide the warping for each layer so that the composited image avoids distortions and holes, maintains good stereoscopic properties and contains as many important pixels as possible in the reduced image space. The proposed method offers the advantages of less discontinuous artifacts, less-distorted objects, correct depth ordering and enhanced stereoscopic quality. Experiments show that our method compares favorably with existing methods.