Integration of Z-Depth in Compositing

It is important for video compositors to be able to complete their jobs quickly and efficiently. One of the tasks they might encounter is to insert assets such as characters into a 3D rendered environment that has depth information embedded into the image sequence. Currently, a plug-in that facilitates this task (Depth Matte®) functions by looking at the depth information of the layer it\u27s applied to and showing or hiding pixels of that layer. In this plug-in, the Z-Depth used is locked to the layer the plug-in is applied. This research focuses on comparing Depth Matte® to a custom-made plug-in that looks at depth information of a layer other than the one it is applied to, yet showing or hiding the pixels of the layer that it is associated with. Nine subjects tested both Depth Matte® and the custom plug-in ZeDI to gather time and mouse-click data. Time was gathered to test speed and mouse-click data was gathered to test efficiency. ZeDI was shown to be significantly quicker and more efficient, and was also overwhelmingly preferred by the users. In conclusion a technique where pixels are shown dependent on depth information that does not necessarily come from the same layer it\u27s applied to, is quicker and more efficient than one where the depth information is locked to the layer that the plug-in is applied

Integration of Z-Depth in Compositing

It is important for video compositors to be able to complete their jobs quickly and efficiently. One of the tasks they might encounter is to insert assets such as characters into a 3D rendered environment that has depth information embedded into the image sequence. Currently, a plug-in that facilitates this task (Depth Matte®) functions by looking at the depth information of the layer it\u27s applied to and showing or hiding pixels of that layer. In this plug-in, the Z-Depth used is locked to the layer the plug-in is applied. This research focuses on comparing Depth Matte® to a custom-made plug-in that looks at depth information of a layer other than the one it is applied to, yet showing or hiding the pixels of the layer that it is associated with. Nine subjects tested both Depth Matte® and the custom plug-in ZeDI to gather time and mouse-click data. Time was gathered to test speed and mouse-click data was gathered to test efficiency. ZeDI was shown to be significantly quicker and more efficient, and was also overwhelmingly preferred by the users. In conclusion a technique where pixels are shown dependent on depth information that does not necessarily come from the same layer it\u27s applied to, is quicker and more efficient than one where the depth information is locked to the layer that the plug-in is applied

FactorMatte: Redefining Video Matting for Re-Composition Tasks

We propose "factor matting", an alternative formulation of the video matting problem in terms of counterfactual video synthesis that is better suited for re-composition tasks. The goal of factor matting is to separate the contents of video into independent components, each visualizing a counterfactual version of the scene where contents of other components have been removed. We show that factor matting maps well to a more general Bayesian framing of the matting problem that accounts for complex conditional interactions between layers. Based on this observation, we present a method for solving the factor matting problem that produces useful decompositions even for video with complex cross-layer interactions like splashes, shadows, and reflections. Our method is trained per-video and requires neither pre-training on external large datasets, nor knowledge about the 3D structure of the scene. We conduct extensive experiments, and show that our method not only can disentangle scenes with complex interactions, but also outperforms top methods on existing tasks such as classical video matting and background subtraction. In addition, we demonstrate the benefits of our approach on a range of downstream tasks. Please refer to our project webpage for more details: https://factormatte.github.ioComment: Project webpage: https://factormatte.github.i

Exploring Defocus Matting: Nonparametric Acceleration, Super-Resolution, and Off-Center Matting

Defocus matting is a fully automatic and passive method for pulling mattes from video captured with coaxial cameras that have different depths of field and planes of focus. Nonparametric sampling can accelerate the video-matting process from minutes to seconds per frame. In addition, a super-resolution technique efficiently bridges the gap between mattes from high-resolution video cameras and those from low-resolution cameras. Off-center matting pulls mattes for an external high-resolution camera that doesn't share the same center of projection as the low-resolution cameras used to capture the defocus matting data.Engineering and Applied Science

Deep Image Matting: A Comprehensive Survey

Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an ill-posed problem, traditional methods have been trying to solve it for decades. The emergence of deep learning has revolutionized the field of image matting and given birth to multiple new techniques, including automatic, interactive, and referring image matting. This paper presents a comprehensive review of recent advancements in image matting in the era of deep learning. We focus on two fundamental sub-tasks: auxiliary input-based image matting, which involves user-defined input to predict the alpha matte, and automatic image matting, which generates results without any manual intervention. We systematically review the existing methods for these two tasks according to their task settings and network structures and provide a summary of their advantages and disadvantages. Furthermore, we introduce the commonly used image matting datasets and evaluate the performance of representative matting methods both quantitatively and qualitatively. Finally, we discuss relevant applications of image matting and highlight existing challenges and potential opportunities for future research. We also maintain a public repository to track the rapid development of deep image matting at https://github.com/JizhiziLi/matting-survey

Multiple View Geometry For Video Analysis And Post-production

Multiple view geometry is the foundation of an important class of computer vision techniques for simultaneous recovery of camera motion and scene structure from a set of images. There are numerous important applications in this area. Examples include video post-production, scene reconstruction, registration, surveillance, tracking, and segmentation. In video post-production, which is the topic being addressed in this dissertation, computer analysis of the motion of the camera can replace the currently used manual methods for correctly aligning an artificially inserted object in a scene. However, existing single view methods typically require multiple vanishing points, and therefore would fail when only one vanishing point is available. In addition, current multiple view techniques, making use of either epipolar geometry or trifocal tensor, do not exploit fully the properties of constant or known camera motion. Finally, there does not exist a general solution to the problem of synchronization of N video sequences of distinct general scenes captured by cameras undergoing similar ego-motions, which is the necessary step for video post-production among different input videos. This dissertation proposes several advancements that overcome these limitations. These advancements are used to develop an efficient framework for video analysis and post-production in multiple cameras. In the first part of the dissertation, the novel inter-image constraints are introduced that are particularly useful for scenes where minimal information is available. This result extends the current state-of-the-art in single view geometry techniques to situations where only one vanishing point is available. The property of constant or known camera motion is also described in this dissertation for applications such as calibration of a network of cameras in video surveillance systems, and Euclidean reconstruction from turn-table image sequences in the presence of zoom and focus. We then propose a new framework for the estimation and alignment of camera motions, including both simple (panning, tracking and zooming) and complex (e.g. hand-held) camera motions. Accuracy of these results is demonstrated by applying our approach to video post-production applications such as video cut-and-paste and shadow synthesis. As realistic image-based rendering problems, these applications require extreme accuracy in the estimation of camera geometry, the position and the orientation of the light source, and the photometric properties of the resulting cast shadows. In each case, the theoretical results are fully supported and illustrated by both numerical simulations and thorough experimentation on real data

Real-Time Chromakey Matting Using Image Statistics

Given a video signal, we generate an alpha matte based on the chromakey information. The computation is done in interactive-time using pixel shaders. To accomplish this, we use Principle Components Analysis to generate a linear transformation matrix where the resulting color triplets Euclidean distance is directly related to the probability that the color exists in the chromakey spectrum. The result of this process is a trimap of the video signals opacity. To solve the alpha matte from the trimap, we minimize an energy function constrained by the trimap with gradient descent. This energy function is based on the least-squared error of overlapping neighborhoods around each pixel and is independent of the background or foreground color