Abstract:
Some representative embodiments are directed to creating a “virtual world” by processing a series of two dimensional images to generate a representation of the physical world depicted in the series of images. The virtual world representation includes models of objects that specify the locations of the objects within the virtual world, the geometries of the objects, the dimensions of the objects, the surface representation of the objects, and/or other relevant information. By developing the virtual world representation, a number of image processing effects may be applied such as generation of stereoscopic images, object insertion, object removal, object translation, and/or other object manipulation operations.

Description:
TECHNICAL FIELD 
     The present invention is generally directed to processing graphical images. 
     BACKGROUND 
     A number of technologies have been proposed and, in some cases, implemented to perform a conversion of one or several two dimensional images into one or several stereoscopic three dimensional images. The conversion of two dimensional images into three dimensional images involves creating a pair of stereoscopic images for each three dimensional frame. The stereoscopic images can then be presented to a viewer&#39;s left and right eyes using a suitable display device. The image information between respective stereoscopic images differ according to the calculated spatial relationships between the objects in the scene and the viewer of the scene. The difference in the image information enables the viewer to perceive the three dimensional effect. 
     An example of a conversion technology is described in U.S. Pat. No. 6,477,267 (the &#39;267 patent). In the &#39;267 patent, only selected objects within a given two dimensional image are processed to receive a three dimensional effect in a resulting three dimensional image. In the &#39;267 patent, an object is initially selected for such processing by outlining the object. The selected object is assigned a “depth” value that is representative of the relative distance of the object from the viewer. A lateral displacement of the selected object is performed for each image of a stereoscopic pair of images that depends upon the assigned depth value. Essentially, a “cut-and-paste” operation occurs to create the three dimensional effect. The simple displacement of the object creates a gap or blank region in the object&#39;s background. The system disclosed in the &#39;267 patent compensates for the gap by “stretching” the object&#39;s background to fill the blank region. 
     The &#39;267 patent is associated with a number of limitations. Specifically, the stretching operations cause distortion of the object being stretched. The distortion needs to be minimized to reduce visual anomalies. The amount of stretching also corresponds to the disparity or parallax between an object and its background and is a function of their relative distances from the observer. Thus, the relative distances of interacting objects must be kept small. 
     Another example of a conversion technology is described in U.S. Pat. No. 6,466,205 (the &#39;205 patent). In the &#39;205 patent, a sequence of video frames is processed to select objects and to create “cells” or “mattes” of selected objects that substantially only include information pertaining to their respective objects. A partial occlusion of a selected object by another object in a given frame is addressed by temporally searching through the sequence of video frames to identify other frames in which the same portion of the first object is not occluded. Accordingly, a cell may be created for the full object even though the full object does not appear in any single frame. The advantage of such processing is that gaps or blank regions do not appear when objects are displaced in order to provide a three dimensional effect. Specifically, a portion of the background or other object that would be blank may be filled with graphical information obtained from other frames in the temporal sequence. Accordingly, the rendering of the three dimensional images may occur in an advantageous manner. 
     SUMMARY 
     Some representative embodiments are directed to creating a “virtual world” by processing a series of two dimensional images to generate a representation of the physical world depicted in the series of images. The virtual world representation includes models of objects that specify the locations of the objects within the virtual world, the geometries of the objects, the dimensions of the objects, the surface representation of the objects, and/or other relevant information. By developing the virtual world representation, a number of image processing effects may be applied. 
     In one embodiment, stereoscopic images may be created. To create a pair of stereoscopic images, two separate views of the virtual world are rendered that correspond to the left and right eyes of the viewer using two different camera positions. Rendering stereoscopic images in this manner produces three dimensional effects of greater perceived quality than possible using known conversion techniques. Specifically, the use of a three dimensional geometry to perform surface reconstruction enables a more accurate representation of objects than possible when two dimensional correlation is employed. 
     In one embodiment, the algorithm analysis and manual input are applied to a series of two dimensional images using an editing application. A graphical user interface of the editing application enables an “editor” to control the operations of the image processing algorithms and camera reconstruction algorithms to begin the creation of the object models. Concurrently with the application of the algorithms, the editor may supply the user input to refine the object models via the graphical user interface. By coordinating manual and autonomous image operations, a two dimensional sequence may be converted into the virtual world representation in an efficient manner. Accordingly, further image processing such as two to three dimension conversation may occur in a more efficient and more accurate manner than possible using known processing techniques. 
     The foregoing has outlined rather broadly the features and technical advantages of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter which form the subject of the claims of the invention. It should be appreciated that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. It should also be realized that such equivalent constructions do not depart from the invention as set forth in the appended claims. The novel features which are believed to be characteristic of the invention, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  depicts key frames of a video sequence. 
         FIG. 2  depicts representations of an object from the video sequence shown in  FIG. 1  generated according to one representative embodiment. 
         FIG. 3  depicts an “overhead” view of a three dimensional scene generated according to one representative embodiment. 
         FIGS. 4 and 5  depict stereoscopic images generated according to one representative embodiment. 
         FIG. 6  depicts a set of interrelated processes for developing a model of a three dimensional scene from a video sequence according to one representative embodiment. 
         FIG. 7  depicts a flowchart for generating texture data according to one representative embodiment. 
         FIG. 8  depicts a system implemented according to one representative embodiment. 
         FIG. 9  depicts a set of frames in which objects may be represented using three dimensional models according to one representative embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring now to the drawings,  FIG. 1  depicts sequence  100  of video images that may be processed according to some representative embodiments. Sequence  100  of video images includes key frames  101 - 104 . Multiple other frames may exist between these key frames. 
     As shown in  FIG. 1 , sphere  150  possesses multiple tones and/or chromatic content. One half of sphere  150  is rendered using first tone  151  and the other half of sphere  150  is rendered using second tone  152 . Sphere  150  undergoes rotational transforms through video sequence  100 . Accordingly, in key frame  102 , a greater amount of tone  151  is seen relative to key frame  101 . In key frame  103 , sufficient rotation has occurred to cause only tone  151  of sphere  150  to be visible. In key frame  104 , tone  152  becomes visible again on the opposite side of sphere  150  as compared to the position of tone  152  in key frame  101 . 
     Box  160  is subjected to scaling transformations in video sequence  100 . Specifically, box  160  becomes smaller throughout video sequence  100 . Moreover, box  160  is translated during video sequence  100 . Eventually, the motion of box  160  causes box  160  to be occluded by sphere  150 . In key frame  104 , box  160  is no longer visible. 
     According to known image processing techniques, the generation of stereoscopic images for key frame  103  would occur by segmenting or matting sphere  150  from key frame  103 . The segmented or matted image data for sphere  150  would consist of a single tone (i.e., tone  151 ). The segmented or matted image data may be displaced in the stereoscopic views. Additionally, image filling or object stretching may occur to address empty regions caused by the displacement. The limitations associated with some known image processing techniques are seen by the inability to accurately render the multi-tone surface characteristics of sphere  150 . Specifically, because the generation of stereoscopic views according to known image processing techniques only uses the matted or segmented image data, known techniques would render sphere  150  as a single-tone object in both the right and left images of a stereoscopic pair of images. However, such rendering deviates from the views that would be actually produced in a three dimensional scene. In an actual three dimensional scene, the right view may cause a portion of tone  152  to be visible on the right side of sphere  150 . Likewise, the left view may cause a portion of tone  152  to be visible on the left side of sphere  150 . 
     Representative embodiments enable a greater degree of accuracy to be achieved when rendering stereoscopic images by creating three dimensional models of objects within the images being processed. A single three dimensional model may be created for box  160 . Additionally, the scaling transformations experienced by box  160  may be encoded with the model created for box  160 . Representations  201 - 204  of box  160  as shown in  FIG. 2  correspond to the key frames  101 - 104 . Additionally, it is noted that box  160  is not explicitly present in key frame  104 . However, because the scaling transformations and translations can be identified and encoded, representation  204  of box  160  may be created for key frame  104 . The creation of a representation for an object that is not visible in a key frame may be useful to enable a number of effects. For example, an object removal operation may be selected to remove sphere  150  thereby causing box  160  to be visible in the resulting processed image(s). 
     In a similar manner, a three dimensional model may be selected or created for sphere  150 . The rotational transform information associated with sphere  150  may be encoded in association with the three dimensional model. 
     Using the three dimensional models and camera reconstruction information, a three dimensional scene including the locations of the objects within the scene may be defined.  FIG. 3  depicts an “overhead” view of scene  300  including three dimensional model  301  of sphere  150  and three dimensional model  302  of box  160  that correspond to key frame  103 . As shown in  FIG. 3 , tone  152  is generally facing away from the viewing perspectives and tone  151  is generally facing toward the viewing perspectives. However, because the right view is slightly offset, a portion of tone  152  is visible. Also, a smaller amount of three dimensional model  302  of box  160  is occluded by three dimensional model  301  of sphere  150 . 
     Using three dimensional scene  300 , left image  400  and right image  500  may be generated as shown in  FIGS. 4 and 5 . Specifically, three dimensional scene  300  defines which objects are visible, the position of the objects, and the sizes of the objects for the left and right views. The rendering of the objects in the views may occur by mapping image data onto the three dimensional objects using texture mapping techniques. The encoded transform information may be used to perform the texture mapping in an accurate manner. For example, the rotation transform information encoded for sphere  150  enables the left portion of sphere  150  to include tone  152  in left image  400 . The transform information enables the right portion of sphere  150  to include tone  152  in right image  500 . Specifically, image data associated with tone  152  in key frames  102  and  104  may be mapped onto the appropriate portions of sphere  150  in images  400  and  500  using the transform information. Likewise, the surface characteristics of the portion of box  160  that has become visible in image  500  may be appropriately rendered using information from key frame  102  and the transform information. 
     To further illustrate the operation of some embodiments, reference is made to  FIG. 9 .  FIG. 9  depict a set of video frames in which a box is rotating in two axes. Using conventional matte modeling techniques, an object matte would be created for each of frames  901 - 904 , because the two dimensional representation of the box is different in each of the frames. The creation of respective object mattes for each of frames  901 - 904  may then be a time consuming and cumbersome process. However, according to one representative embodiment, an object model is created for frame  901 . Because the three dimensional characteristics of the box do not change, only the rotation information may be defined for frames  902 - 904 . The surface characteristics of the box can then be autonomously extracted from frames  902 - 904  using the object model and the transform information. Thus, some representative embodiments provide a more efficient process for processing video frames than conventional techniques. 
       FIG. 6  depicts an interrelated set of processes for defining three dimensional objects from video images according to one representative embodiment. In process  601 , outlines of objects of interest are defined in selected frames. The outline of the objects may occur in a semi-autonomous manner. The user may manually select a relatively small number of points of the edge of a respective object. An edge tracking algorithm may then be used to identify the outline of the object between the user selected points. In general, edge tracking algorithms operate by determining the least path cost between two points where the path cost is a function of image gradient characteristics. Domain-specific information concerning the selected object may also be employed during edge tracking. A series of Bezier curves or other parametric curves may be used to encode the outlines of the objects. Further user input may be used to refine the curves if desired. 
     In process  602 , camera reconstruction may be performed. Camera reconstruction refers to the process in which the relationship between the camera and the three dimensional scene(s) in the video sequence is analyzed. During this process, the camera&#39;s focal length, the camera&#39;s relative angular perspective, the camera&#39;s position and orientation relative to objects in the scene, and/or other suitable information may be estimated. 
     In process  603 , three dimensional models are created or selected from a library of predefined three dimensional models for the objects. Any number of suitable model formats could be used. For example, Constructive Solid Geometry models could be employed in which each object is represented as a combination of object primitives (e.g., blocks, cylinders, cones, spheres, etc.) and logical operations on the primitives (e.g., union, difference, intersection, etc.). Additionally or alternatively, nonuniform rational B-splines (NURBS) models could be employed in which objects are defined in terms of sets of weighted control points, curve orders, and knot vectors. Additionally, “skeleton” model elements could be defined to facilitate image processing associated with complex motion of an object through a video sequence according to kinematic animation techniques. 
     In process  664 , transformations and translations are defined as experienced by the objects of interest between key frames. Specifically, the translation or displacement of objects, the scaling of objects, the rotation of objects, morphing of objects, and/or the like may be defined. For example, an object may increase in size between key frames. The increase in size may result from the object approaching the camera or from the object actually become larger (“ballooning”). By accurately encoding whether the object has been increased in size as opposed to merely moving in the three dimensional scene, subsequent processing may occur more accurately. This step may be performed using a combination of autonomous algorithms and user input. For example, motion compensation algorithms may be used to estimate the translation of objects. If an object has experienced scaling, the user may identify that scaling has occurred and an autonomous algorithm may calculate a scaling factor by comparing image outlines between the key frames. 
     In process  605 , using the information developed in the prior steps, the positions of objects in the three dimensional scene(s) of the video sequence are defined. The definition of the positions may occur in an autonomous manner. User input may be received to alter the positions of objects for editing or other purposes. Additionally, one or several objects may be removed if desired. 
     In process  606 , surface property data structures, such as texture maps, are created. 
       FIG. 7  depicts a flowchart for creating texture map data for a three dimensional object for a particular temporal position according to one representative embodiment. The flowchart for creating texture map data begins in step  701  where a video frame is selected. The selected video frame identifies the temporal position for which the texture map generation will occur. In step  702 , an object from the selected video frame is selected. 
     In step  703 , surface positions of the three dimensional model that correspond to visible portions of the selected object in the selected frame are identified. The identification of the visible surface positions may be performed, as an example, by employing ray tracing from the original camera position to positions on the three dimensional model using the camera reconstruction data. In step  704 , texture map data is created from image data in the selected frame for the identified portions of the three dimensional model. 
     In step  706 , surface positions of the three dimensional model that correspond to portions of the object that were not originally visible in the selected frame are identified. In one embodiment, the entire remaining surface positions are identified in step  706  thereby causing as much texture map data to be created for the selected frame as possible. In certain situations, it may be desirable to limit construction of the texture data. For example, if texture data is generated on demand, it may be desirable to only identify surface positions in this step (i) that correspond to portions of the object not originally visible in the selected frame and (ii) that have become visible due to rendering the object according to a modification in the viewpoint. In this case, the amount of the object surface exposed due to the perspective change can be calculated from the object&#39;s camera distance and a maximum inter-ocular constant. 
     In step  706 , the surface positions identified in step  705  are correlated to image data in frames prior to and/or subsequent to the selected frame using the defined model of the object, object transformations and translations, and camera reconstruction data. In step  707 , the image data from the other frames is subjected to processing according to the transformations, translations, and camera reconstruction data. For example, if a scaling transformation occurred between frames, the image data in the prior or subject frame may be either enlarged or reduced depending upon the scaling factor. Other suitable processing may occur. In one representative embodiment, weighted average processing may be used depending upon how close in the temporal domain the correlated image data is to the selected frame. For example, lighting characteristics may change between frames. The weighted averaging may cause darker pixels to be lightened to match the lighting levels in the selected frame. In one representative embodiment, light sources are also modeled as objects. When models are created for light sources, lighting effects associated with the modeled objects may be removed from the generated textures. The lighting effects would then be reintroduced during rendering. 
     In step  708 , texture map data is created for the surface positions identified in step  705  from the data processed in step  707 . Because the translations, transformations, and other suitable information are used in the image data processing, the texture mapping of image data from other frames onto the three dimensional models occurs in a relatively accurate manner. Specifically, significant discontinuities and other imaging artifacts generally will not be observable. 
     In one representative embodiment, steps  704 - 707  are implemented in association with generating texture data structures that represent the surface characteristics of an object of interest. A given set of texture data structures define all of the surface characteristics of an object that may be recovered from a video sequence. Also, because the surface characteristics may vary over time, a texture data structure may be assigned for each relevant frame. Accordingly, the texture data structures may be considered to capture video information related to a particular object. 
     The combined sets of data (object model, transform information, camera reconstruction information, and texture data structures) enables construction of a three dimensional world from the video sequence. The three dimensional world may be used to support any number of image processing effects. As previously mentioned, stereoscopic images may be created. The stereoscopic images may approximately correspond to the original two dimensional viewpoint. Alternatively, stereoscopic images may be decoupled from the viewpoint(s) of the original video if image data is available from a sufficient number of perspectives. Additionally, object removal may be performed to remove objects from frames of a video sequence. Likewise, object insertion may be performed. 
       FIG. 8  depicts system  800  for processing a sequence of video images according to one representative embodiment. System  800  may be implemented on a suitable computer platform. System  800  includes conventional computing resources such as central processing unit  801 , random access memory (RAM)  802 , read only memory (ROM)  803 , user-peripherals (e.g., keyboard, mouse, etc.)  804 , and display  805 . System  800  further includes non-volatile storage  806 . 
     Non-volatile storage  806  comprises data structures and software code or instructions that enable conventional processing resources to implement some representative embodiments. The data structures and code may implement the flowcharts of  FIGS. 6 and 7  as examples. 
     As shown in  FIG. 8 , non-volatile storage  806  comprises video sequence  807 . Video sequence  807  may be obtained in digital form from another suitable medium (not shown). Alternatively, video sequence  807  may be obtained after analog-to-digital conversation of an analog video signal from an imaging device (e.g., a video cassette player or video camera). Object matting module  814  defines outlines of selected objects using a suitable image processing algorithm or algorithms and user input. Camera reconstruction algorithm  817  processes video sequence  807  to determine the relationship between objects in video sequence  807  and the camera used to capture the images. Camera reconstruction algorithm  817  stores the data in camera reconstruction data  811 . 
     Model selection module  815  enables model templates from model library  810  to be associated with objects in video sequence  807 . The selection of models for objects are stored in object models  808 . Object refinement module  816  generates and encodes transformation data within object models  808  in video sequence  807  using user input and autonomous algorithms. Object models  808  may represent an animated geometry encoding shape, transformation, and position data over time. Object models  808  may be hierarchical and may have an associated template type (e.g., a chair). 
     Texture map generation module  821  generates textures that represent the surface characteristics of objects in video sequence  807 . Texture map generation module  821  uses object models  808  and camera data  811  to generate texture map data structures  809 . Preferably, each object comprises a texture map for each key frame that depicts as much surface characteristics as possible given the number of perspectives in video sequence  807  of the objects and the occlusions of the objects. In particular, texture map generation module  821  performs searches in prior frames and/or subsequent frames to obtain surface characteristic data that is not present in a current frame. The translation and transform data is used to place the surface characteristics from the other frames in the appropriate portions of texture map data structures  809 . Also, the transform data may be used to scale, morph, or otherwise process the data from the other frames so that the processed data matches the characteristics of the texture data obtained from the current frame. Texture refinement module  822  may be used to perform user editing of the generated textures if desired. 
     Scene editing module  818  enables the user to define how processed image data  820  is to be created. For example, the user may define how the left and right perspectives are to be defined for stereoscopic images if a three dimensional effect is desired. Alternatively, the user may provide suitable input to create a two dimensional video sequence having other image processing effects if desired. Object insertion and removal may occur through the receipt of user input to identify objects to be inserted and/or removed and the frames for these effects. Additionally, the user may change object positions. 
     When the user finishes inputting data via scene editing module  818 , the user may employ rendering algorithm  819  to generate processed image data  820 . Processed image data  820  is constructed using object models  808 , texture map data structures  809 , and other suitable information to provide the desired image processing effects. 
     Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one will readily appreciate from the disclosure, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.