This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specified by either an image or text. Existing methods struggle to fully meet the task's challenging objectives: (i) seamlessly composing the object into the scene with photorealistic pose and lighting, and (ii) preserving the object's identity. We hypothesize that achieving these goals requires large scale supervision, but manually collecting sufficient data is simply too expensive. The key observation in this paper is that many mass-produced objects recur across multiple images of large unlabeled datasets, in different scenes, poses, and lighting conditions. We use this observation to create massive supervision by retrieving sets of diverse views of the same object. This powerful paired dataset enables us to train a straightforward text-to-image diffusion architecture to map the object and scene descriptions to the composited image. We compare our method, ObjectMate, with state-of-the-art methods for object insertion and subject-driven generation, using a single or multiple references. Empirically, ObjectMate achieves superior identity preservation and more photorealistic composition. Differently from many other multi-reference methods, ObjectMate does not require slow test-time tuning.

We present a new image morphing approach in which the output sequence is regenerated from small pieces of the two source (input) images. The approach does not require manual correspondence, and generates compelling results even when the images are of very different objects (e.g., a cloud and a face). We pose the morphing task as an optimization with the objective of achieving bidirectional similarity of each frame to its neighbors, and also to the source images. The advantages of this approach are 1) it can operate fully automatically, producing effective results for many sequences (but also supports manual correspondences, when available), 2) ghosting artifacts are minimized, and 3) different parts of the scene move at different rates, yielding more interesting (and less robotic) transitions.

We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques.

We present spectral matting: a new approach to natural image matting that automatically computes a basis set of fuzzy matting components from the smallest eigenvectors of a suitably defined Laplacian matrix. Thus, our approach extends spectral segmentation techniques, whose goal is to extract hard segments, to the extraction of soft matting components. These components may then be used as building blocks to easily construct semantically meaningful foreground mattes, either in an unsupervised fashion, or based on a small amount of user input.

The amount of captured video is growing with the increased numbers of video cameras, especially the increase of millions of surveillance cameras that operate 24 hours a day. Since video browsing and retrieval is time consuming, most captured video is never watched or examined. Video synopsis is an effective tool for browsing and indexing of such a video. It provides a short video representation, while preserving the essential activities of the original video. The activity in the video is condensed into a shorter period by simultaneously showing multiple activities, even when they originally occurred at different times. The synopsis video is also an index into the original video by pointing to the original time of each activity. Video Synopsis can be applied to create a synopsis of an endless video streams, as generated by webcams and by surveillance cameras. It can address queries like "Show in one minute the synopsis of this camera broadcast during the past day''. This process includes two major phases: (i) An online conversion of the endless video stream into a database of objects and activities (rather than frames). (ii) A response phase, generating the video synopsis as a response to the user's query.

We introduce a new representation for video which facilitates a number of common editing tasks. The representation has some of the power of a full reconstruction of 3D surface models from video, but is designed to be easy to recover from a priori unseen and uncalibrated footage. By modelling the image-formation process as a 2D-to-2D transformation from an object's texture map to the image, modulated by an object-space occlusion mask, we can recover a representation which we term the "unwrap mosaic". Many editing operations can be performed on the unwrap mosaic, and then re-composited into the original sequence, for example resizing objects, repainting textures, copying/cutting/pasting objects, and attaching effects layers to deforming objects.

We present spectral matting: a new approach to natural image matting that automatically computes a set of fundamental fuzzy matting components from the smallest eigenvectors of a suitably defined Laplacian matrix. Thus, our approach extends spectral segmentation techniques, whose goal is to extract hard segments, to the extraction of soft matting components. These components may then be used as building blocks to easily construct semantically meaningful foreground mattes, either in an unsupervised fashion, or based on a small amount of user input.

The world is covered with millions of Webcams, many transmit everything in their field of view over the Internet 24 hours a day. A Web search finds public webcams in airports, intersections, classrooms, parks, shops, ski resorts, and more. Even more private surveillance cameras cover many private and public facilities. Webcams are an endless resource, but most of the video broadcast will be of little interest due to lack of activity. We propose to generate a short video that will be a synopsis of an endless video streams, generated by webcams or surveillance cameras. We would like to address queries like "I would like to watch in one minute the highlights of this camera broadcast during the past day". The process includes two major phases: (i) An online conversion of the video stream into a database of objects and activities (rather than frames), (ii) A response phase, generating the video synopsis as a response to the user's query. To include maximum information in a short synopsis we simultaneously show activities that may have happened at different times. The synopsis video can also be used as an index into the original video stream.

This paper explores the manipulation of time in video editing, enabling to control the chronological time of events. These time manipulations include slowing down (or postponing) some dynamic events while speeding up (or advancing) others. When a video camera scans a scene, aligning all the events to a single time interval will result in a panoramic movie. Time manipulations are obtained by first constructing an aligned space-time volume from the input video, and then sweeping a continuous 2D slice (time front) through that volume, generating a new sequence of images. For dynamic scenes, aligning the input video frames poses an important challenge. We propose to align dynamic scenes using a new notion of "dynamics constancy", which is more appropriate for this task than the traditional assumption of "brightness constancy". Another challenge is to avoid visual seams inside moving objects and other visual artifacts resulting from sweeping the space-time volumes with time fronts of arbitrary geometry. To avoid such artifacts, we formulate the problem of finding optimal time front geometry as one of finding a minimal cut in a 4D graph, and solve it using max-flow methods.

The power of video over still images is the ability to represent dynamic activities. But video browsing and retrieval are inconvenient due to inherent spatio-temporal redundancies, where some time intervals may have no activity, or have activities that occur in a small image region. Video synopsis aims to provide a compact video representation, while preserving the essential activities of the original video. We present dynamic video synopsis, where most of the activity in the video is condensed by simultaneously showing several actions, even when they originally occurred at different times. For example, we can create a "stroboscopic movie", where multiple dynamic instances of a moving object are played simultaneously. This is an extension of the still stroboscopic picture. Previous approaches for video abstraction addressed mostly the temporal redundancy by selecting representative key-frames or time intervals. In dynamic video synopsis the activity is shifted into a significantly shorter period, in which the activity is much denser. Video examples can be found online in http://www.vision.huji.ac.il/synopsis

Many methods for motion computation and object tracking are based on the Lucas-Kanade (LK) framework. We present a method which substantially speeds up the LK approach while preserving its accuracy. This acceleration is obtained by avoiding the iterative image warping, inherent to the LK framework. A three-fold speedup is observed on standard image alignment tasks. Our second contribution focuses on adopting a multi-frame approach in order to increase alignment accuracy and robustness. By utilizing the acceleration procedure, the complexity of this multi-frame alignment becomes comparable to that of the two-frame approach.

With the limited field of view of human vision, our perception of most scenes is built over time while our eyes are scanning the scene. In the case of static scenes, this process can be modeled by panoramic mosaicing: stitching together images into a panoramic view. Can a dynamic scene, scanned by a video camera, be represented with a dynamic panoramic video even though different regions were visible at different times? In this paper, we explore time flow manipulation in video, such as the creation of new videos in which events that occurred at different times are displayed simultaneously. More general changes in the time flow are also possible, which enable re-scheduling the order of dynamic events in the video, for example. We generate dynamic mosaics by sweeping the aligned space-time volume of the input video by a time front surface and generating a sequence of time slices in the process. Various sweeping strategies and different time front evolutions manipulate the time flow in the video, enabling many unexplored and powerful effects, such as panoramic movies.

A robust approach for super-resolution is, presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion errors, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an iterative process to achieve a super resolution algorithm. This process can increase resolution even in regions with outliers, where other super resolution methods actually degrade the image.

Images degraded by motion blur can be restored when several blurred images are given, and the direction of motion blur in each image is different. Given two motion blurred images, best restoration is obtained when the directions of motion blur in the two images are orthogonal. Motion blur at different directions is common, for example, in the case of small hand-held digital cameras due to fast hand trembling and the light weight of the camera. Restoration examples are given on simulated data as well as on images with real motion blur.

Image mosaicing is commonly used to increase the visual field of view by pasting together many images or video frames. Existing mosaicing methods are based on projecting all images onto a predetermined single manifold: A plane is commonly used for a camera translating sideways, a cylinder is used for a panning camera, and a sphere is used for a camera which is both panning and tilting. While different mosaicing methods should therefore be used for different types of camera motion, more general types of camera motion, such as forward motion, are practically impossible for traditional mosaicing. A new methodology to allow image mosaicing in more general cases of camera motion is presented. Mosaicing is performed by projecting thin strips from the images onto manifolds which are adapted to the camera motion. While the limitations of existing mosaicing techniques are a result of using predetermined manifolds, the use of more general manifolds overcomes these limitations.

Video mosaicing is commonly used to increase the visual field by pasting together many video frames. Existing mosaicing methods are effective only in very limited cases where the image motion is almost a uniform translation or the camera performs a pure pan. Forward camera motion or camera zoom are very problematic for traditional mosaicing. A mosaicing methodology to allow image mosaicing in the most general cases is presented, where frames in the video sequence are transformed such that the optical flow becomes parallel. This transformation is an oblique projection of the image into a "viewing pipe" whose central axis is the trajectory of the camera. The "pipe projection" enables to define high quality mosaicing even for the most challenging cases of forward motion and of zoom. In addition view interpolation, generating dense intermediate views is used to overcome parallax effects.