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When motion capture data is applied to virtual characters, the applied motion often exhibits geometric and physical errors, which necessitates a cumbersome refinement process. We present a novel framework to efficiently obtain a corrected motion as well as its supporting contact information from multi-contact motion capture data. To this end, first, we present a projective dynamics-based method for optimizing character motions. By carefully defining objective functions and constraints using differential representation of motions, we develop a highly efficient motion optimizer that can create geometrically and dynamically adjusted motions given reference motion data and contact information. Second, we develop a contact optimizer that finds a set of contacts that allows the motion optimizer to generate a motion that best follows the reference motion under dynamic and geometric constraints. This is achieved by iteratively improving the hypothesis on the best set of contacts by getting feedback from the motion optimizer. We demonstrate that our method significantly improves the naturalness of a wide range of motion capture data, from walking to rolling. [ABSTRACT FROM AUTHOR]

A narrative collage is an interesting image editing method for summarizing the main theme or storyline behind an image collection. We present a novel method to generate narrative images with plausible semantic scene structures. To achieve this goal, we introduce a layer graph and a scene graph to represent the relative depth order and semantic relationship between image objects, respectively. We first cluster the input image collection to select representative images, and then we extract a group of semantic salient objects from each representative image. Both layer graphs and scene graphs are constructed and combined according to our specific rules for reorganizing the extracted objects in every image. We design an energy model to appropriately locate every object on the final canvas. The experimental results show that our method can produce competitive narrative collage results and that it performs well on a wide range of image collections. [ABSTRACT FROM AUTHOR]

Patch-based image synthesis methods have been successfully applied for various editing tasks on still images, videos and stereo pairs. In this work we extend patch-based synthesis to plenoptic images captured by consumer-level lenselet-based devices for interactive, efficient light field editing. In our method the light field is represented as a set of images captured from different viewpoints. We decompose the central view into different depth layers, and present it to the user for specifying the editing goals. Given an editing task, our method performs patch-based image synthesis on all affected layers of the central view, and then propagates the edits to all other views. Interaction is done through a conventional 2D image editing user interface that is familiar to novice users. Our method correctly handles object boundary occlusion with semi-transparency, thus can generate more realistic results than previous methods. We demonstrate compelling results on a wide range of applications such as hole-filling, object reshuffling and resizing, changing object depth, light field upscaling and parallax magnification. [ABSTRACT FROM PUBLISHER]

With a growth of surveillance cameras, the amount of captured videos expands. Manually analyzing and retrieving surveillance video is labor intensive and expensive. It would be much more convenient to generate a video digest, with which we can view the video in a fast and motion-preserving way. In this paper, we propose a novel video synopsis approach to generate condensed video, which uses an object tracking method for extracting important objects. This method will generate video tubes and a seam carving method to condense the original video. Experimental results demonstrate that our proposed method can achieve a high condensation rate while preserving all the important objects of interest. Therefore, this approach can enable users to view the surveillance video with great efficiency. [ABSTRACT FROM PUBLISHER]

This paper presents a patch-based synthesis framework for stereoscopic image editing. The core of the proposed method builds upon a patch-based optimization framework with two key contributions: First, we introduce a depth-dependent patch-pair similarity measure for distinguishing and better utilizing image contents with different depth structures. Second, a joint patch-pair search is proposed for properly handling the correlation between two views. The proposed method successfully overcomes two main challenges of editing stereoscopic 3D media: (1) maintaining the depth interpretation, and (2) providing controllability of the scene depth. The method offers patch-based solutions to a wide variety of stereoscopic image editing problems, including depth-guided texture synthesis, stereoscopic NPR, paint by depth, content adaptation, and 2D to 3D conversion. Several challenging cases are demonstrated to show the effectiveness of the proposed method. The results of user studies also show that the proposed method produces stereoscopic images with good stereoscopics and visual quality. [ABSTRACT FROM PUBLISHER]

Traditional image editing techniques cannot be directly used to edit stereoscopic ("3D”) media, as extra constraints are needed to ensure consistent changes are made to both left and right images. Here, we consider manipulating perspective in stereoscopic pairs. A straightforward approach based on depth recovery is unsatisfactory: Instead, we use feature correspondences between stereoscopic image pairs. Given a new, user-specified perspective, we determine correspondence constraints under this perspective and optimize a 2D warp for each image that preserves straight lines and guarantees proper stereopsis relative to the new camera. Experiments verify that our method generates new stereoscopic views that correspond well to expected projections, for a wide range of specified perspective. Various advanced camera effects, such as dolly zoom and wide angle effects, can also be readily generated for stereoscopic image pairs using our method. [ABSTRACT FROM AUTHOR]

The state-of-the-art interactive image segmentation algorithms are sensitive to the user inputs and often unable to produce an accurate boundary with a small amount of user interaction. They frequently rely on laborious user editing to refine the segmentation boundary. In this paper, we propose a robust and accurate interactive method based on the recently developed continuous-domain convex active contour model. The proposed method exhibits many desirable properties of an effective interactive image segmentation algorithm, including robustness to user inputs and different initializations, the ability to produce a smooth and accurate boundary contour, and the ability to handle topology changes. Experimental results on a benchmark data set show that the proposed tool is highly effective and outperforms the state-of-the-art interactive image segmentation algorithms. [ABSTRACT FROM AUTHOR]

How to recover a clear image from a single motion-blurred image has long been a challenging open problem in digital imaging. In this paper, we focus on how to recover a motion-blurred image due to camera shake. A regularization-based approach is proposed to remove motion blurring from the image by regularizing the sparsity of both the original image and the motion-blur kernel under tight wavelet frame systems. Furthermore, an adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problem. The experiments on both synthesized images and real images show that our algorithm can effectively remove complex motion blurring from natural images without requiring any prior information of the motion-blur kernel. [ABSTRACT FROM AUTHOR]

This work is concerned with the modification of the gray level or color distribution of digital images. A common drawback of classical methods aiming at such modifications is the revealing of artefacts or the attenuation of details and textures. In this work, we propose a generic filtering method enabling, given the original image and the radiometrically corrected one, to suppress artefacts while preserving details. The approach relies on the key observation that artefacts correspond to spatial irregularity of the so-called transportation map, defined as the difference between the original and the corrected image. The proposed method draws on the nonlocal Yaroslavsky filter to regularize the transportation map. The efficiency of the method is shown on various radiometric modifications: contrast equalization, midway histogram, color enhancement, and color transfer. A comparison with related approaches is also provided. [ABSTRACT FROM PUBLISHER]

One weakness in the existing interactive image segmentation algorithms is the lack of more intelligent ways to understand the intention of user inputs. In this paper, we advocate the use of multiple intuitive user inputs to better reflect a user's intention. In particular, we propose a constrained random walks algorithm that facilitates the use of three types of user inputs: 1) foreground and background seed input, 2) soft constraint input, and 3) hard constraint input, as well as their combinations. The foreground and background seed input allows a user to draw strokes to specify foreground and background seeds. The soft constraint input allows a user to draw strokes to indicate the region that the boundary should pass through. The hard constraint input allows a user to specify the pixels that the boundary must align with. Our proposed method supports all three types of user inputs in one coherent computational framework consisting of a constrained random walks and a local editing algorithm, which allows more precise contour refinement. Experimental results on two benchmark data sets show that the proposed framework is highly effective and can quickly and accurately segment a wide variety of natural images with ease. [ABSTRACT FROM AUTHOR]

While the state-of-the-art image composition algorithms subtly handle the object boundary to achieve seamless image copy-and-paste, it is observed that they are unable to preserve the color fidelity of the source object, often require quite an amount of user interactions, and often fail to achieve realism when there exists salient discrepancy between the background textures in the source and destination images. These observations motivate our research towards color controlled natural and seamless image composition with least user interactions. In particular, based on the Poisson image editing framework, we first propose a variational model that considers both the gradient constraint and the color fidelity. The proposed model allows users to control the coloring effect caused by gradient domain fusion. Second, to have less user interactions, we propose a distance-enhanced random walks algorithm, through which we avoid the necessity of accurate image segmentation while still able to highlight the foreground object. Third, we propose a multiresolution framework to perform image compositions at different subbands so as to separate the texture and color components to simultaneously achieve smooth texture transition and desired color control. The experimental results demonstrate that our proposed framework achieves better and more realistic results for images with salient background color or texture differences, while providing comparable results as the state-of-the-art algorithms for images without the need of preserving the object color fidelity and without significant background texture discrepancy. [ABSTRACT FROM AUTHOR]

This issue's article examines the digital artwork of Rod Seeley, who transforms basic fractal images into complex works of art. [ABSTRACT FROM AUTHOR]