Abstract: In applications that display a representation of a user, it may be reasonable to insert a pre-canned animation rather than animating a user's captured motion. For example, in a tennis swing, the ball toss and take back in a serve could be a pre-canned animation, whereas the actual forward swing may be mapped from the user's gestures. An animation of a user's gestures can be chained together into sequences with pre-canned animations, where animation blending techniques can provide for a smoother transition between the animation types. Techniques for blending animations, that may comprise determining boundaries and transition points between pre-canned animations and animations based on captured motion, may improve animation efficiency. Gesture history, including joint position, velocity, and acceleration, can be used to determine user intent, seed parameters for subsequent animations and game control, and determine the subsequent gestures to initiate.

Abstract: In applications that display a representation of a user, it may be reasonable to insert a pre-canned animation rather than animating a user's captured motion. For example, in a tennis swing, the ball toss and take back in a serve could be a pre-canned animation, whereas the actual forward swing may be mapped from the user's gestures. An animation of a user's gestures can be chained together into sequences with pre-canned animations, where animation blending techniques can provide for a smoother transition between the animation types. Techniques for blending animations, that may comprise determining boundaries and transition points between pre-canned animations and animations based on captured motion, may improve animation efficiency. Gesture history, including joint position, velocity, and acceleration, can be used to determine user intent, seed parameters for subsequent animations and game control, and determine the subsequent gestures to initiate.

Abstract: A system may receive image data and capture motion with respect to a target in a physical space and recognize a gesture from the captured motion. It may be desirable to isolate aspects of captured motion to differentiate random and extraneous motions. For example, a gesture may comprise motion of a user's right arm, and it may be desirable to isolate the motion of the user's right arm and exclude an interpretation of any other motion. Thus, the isolated aspect may be the focus of the received data for gesture recognition. Alternately, the isolated aspects may be an aspect of the captured motion that is removed from consideration when identifying a gesture from the captured motion. For example, gesture filters may be modified to correspond to the user's natural lean to eliminate the effect the lean has on the registry of a motion with a gesture filter.

Abstract: In applications that display a representation of a user, it may be reasonable to insert a pre-canned animation rather than animating a user's captured motion. For example, in a tennis swing, the ball toss and take back in a serve could be a pre-canned animation, whereas the actual forward swing may be mapped from the user's gestures. An animation of a user's gestures can be chained together into sequences with pre-canned animations, where animation blending techniques can provide for a smoother transition between the animation types. Techniques for blending animations, that may comprise determining boundaries and transition points between pre-canned animations and animations based on captured motion, may improve animation efficiency. Gesture history, including joint position, velocity, and acceleration, can be used to determine user intent, seed parameters for subsequent animations and game control, and determine the subsequent gestures to initiate.

Abstract: A capture device may capture a user's motion and a display device may display a model that maps to the user's motion, including gestures that are applicable for control. A user may be unfamiliar with a system that maps the user's motions or not know what gestures are applicable for an executing application. A user may not understand or know how to perform gestures that are applicable for the executing application. Providing visual feedback representing instructional gesture data to the user can teach the user how to properly gesture. The visual feedback may be provided in any number of suitable ways. For example, visual feedback may be provided via ghosted images, player avatars, or skeletal representations. The system can process prerecorded or live content for displaying visual feedback representing instructional gesture data. The feedback can portray the deltas between the user's actual position and the ideal gesture position.

Abstract: A capture device may capture a user's motion and a display device may display a model that maps to the user's motion, including gestures that are applicable for control. A user may be unfamiliar with a system that maps the user's motions or not know what gestures are applicable for an executing application. A user may not understand or know how to perform gestures that are applicable for the executing application. Providing visual feedback representing instructional gesture data to the user can teach the user how to properly gesture. The visual feedback may be provided in any number of suitable ways. For example, visual feedback may be provided via ghosted images, player avatars, or skeletal representations. The system can process prerecorded or live content for displaying visual feedback representing instructional gesture data. The feedback can portray the deltas between the user's actual position and the ideal gesture position.

Abstract: Virtual objects are located for display in a head-mounted display (HMD) to provide an augment reality view to an HMD wearer. An HMD wearer's total field of view (TFOV) is classified into two or more regions. Additionally, a field of view (FOV) for the HMD wearer is determined. The FOV is compared with the two or more regions of the HMD wearer's TFOV. Responsive to this comparison, virtual objects are determined for display based on a location of the FOV relative to the two or more regions of the HMD wearer's TFOV. The virtual objects may then be displayed by the HMD at an appropriate location.

Abstract: Virtual objects are located for display in a head-mounted display (HMD) to provide an augment reality view to an HMD wearer. An HMD wearer's total field of view (TFOV) is classified into two or more regions. Additionally, a field of view (FOV) for the HMD wearer is determined. The FOV is compared with the two or more regions of the HMD wearer's TFOV. Responsive to this comparison, virtual objects are determined for display based on a location of the FOV relative to the two or more regions of the HMD wearer's TFOV. The virtual objects may then be displayed by the HMD at an appropriate location.

Abstract: In applications that display a representation of a user, it may be reasonable to insert a pre-canned animation rather than animating a user's captured motion. For example, in a tennis swing, the ball toss and take back in a serve could be a pre-canned animation, whereas the actual forward swing may be mapped from the user's gestures. An animation of a user's gestures can be chained together into sequences with pre-canned animations, where animation blending techniques can provide for a smoother transition between the animation types. Techniques for blending animations, that may comprise determining boundaries and transition points between pre-canned animations and animations based on captured motion, may improve animation efficiency. Gesture history, including joint position, velocity, and acceleration, can be used to determine user intent, seed parameters for subsequent animations and game control, and determine the subsequent gestures to initiate.

Abstract: Virtual images are located for display in a head-mounted display (HMD) to provide an augment reality view to an HMD wearer. Sensor data may be collected from on-board sensors provided on an HMD. Additionally, other day may be collected from external sources. Based on the collected sensor data and other data, the position and rotation of the HMD wearer's head relative to the HMD wearer's body and surrounding environment may be determined. After resolving the HMD wearer's head position, the HMD wearer's total field of view (TFOV) may be classified into regions. Virtual images may then be located in the classified TFOV regions to locate the virtual images relative to the HMD wearer's body and surrounding environment.

Abstract: A system may receive image data and capture motion with respect to a target in a physical space and recognize a gesture from the captured motion. It may be desirable to isolate aspects of captured motion to differentiate random and extraneous motions. For example, a gesture may comprise motion of a user's right arm, and it may be desirable to isolate the motion of the user's right arm and exclude an interpretation of any other motion. Thus, the isolated aspect may be the focus of the received data for gesture recognition. Alternately, the isolated aspects may be an aspect of the captured motion that is removed from consideration when identifying a gesture from the captured motion. For example, gesture filters may be modified to correspond to the user's natural lean to eliminate the effect the lean has on the registry of a motion with a gesture filter.

Abstract: In applications that display a representation of a user, it may be reasonable to insert a pre-canned animation rather than animating a user's captured motion. For example, in a tennis swing, the ball toss and take back in a serve could be a pre-canned animation, whereas the actual forward swing may be mapped from the user's gestures. An animation of a user's gestures can be chained together into sequences with pre-canned animations, where animation blending techniques can provide for a smoother transition between the animation types. Techniques for blending animations, that may comprise determining boundaries and transition points between pre-canned animations and animations based on captured motion, may improve animation efficiency. Gesture history, including joint position, velocity, and acceleration, can be used to determine user intent, seed parameters for subsequent animations and game control, and determine the subsequent gestures to initiate.

Abstract: A system may receive image data and capture motion with respect to a target in a physical space and recognize a gesture from the captured motion. It may be desirable to isolate aspects of captured motion to differentiate random and extraneous motions. For example, a gesture may comprise motion of a user's right arm, and it may be desirable to isolate the motion of the user's right arm and exclude an interpretation of any other motion. Thus, the isolated aspect may be the focus of the received data for gesture recognition. Alternately, the isolated aspects may be an aspect of the captured motion that is removed from consideration when identifying a gesture from the captured motion. For example, gesture filters may be modified to correspond to the user's natural lean to eliminate the effect the lean has on the registry of a motion with a gesture filter.

Abstract: It may be desirable to apply corrective data to aspects of captured image or the user-performed gesture for display of a visual representation that corresponds to the corrective data. The captured motion may be any motion in the physical space that is captured by the capture device, such as a camera. Aspects of a skeletal or mesh model of a person, that is generated based on the image data captured by the capture device, may be modified prior to animation. The modification may be made to the model generated from image data that represents a target or a target's motion, including user gestures, in the physical space. For example, certain joints of a skeletal model may be readjusted or realigned. A model of a target may be modified by applying differential correction, magnetism principles, binary snapping, confining virtual movement to defined spaces, or the like.

Abstract: Virtual images are located for display in a head-mounted display (HMD) to provide an augment reality view to an HMD wearer. Sensor data may be collected from on-board sensors provided on an HMD. Additionally, other day may be collected from external sources. Based on the collected sensor data and other data, the position and rotation of the HMD wearer's head relative to the HMD wearer's body and surrounding environment may be determined. After resolving the HMD wearer's head position, the HMD wearer's total field of view (TFOV) may be classified into regions. Virtual images may then be located in the classified TFOV regions to locate the virtual images relative to the HMD wearer's body and surrounding environment.

Abstract: Virtual images are located for display in a head-mounted display (HMD) to provide an augment reality view to an HMD wearer. Sensor data may be collected from on-board sensors provided on an HMD. Additionally, other day may be collected from external sources. Based on the collected sensor data and other data, the position and rotation of the HMD wearer's head relative to the HMD wearer's body and surrounding environment may be determined. After resolving the HMD wearer's head position, the HMD wearer's total field of view (TFOV) may be classified into regions. Virtual images may then be located in the classified TFOV regions to locate the virtual images relative to the HMD wearer's body and surrounding environment.

Abstract: It may be desirable to apply corrective data to aspects of captured image or the user-performed gesture for display of a visual representation that corresponds to the corrective data. The captured motion may be any motion in the physical space that is captured by the capture device, such as a camera. Aspects of a skeletal or mesh model of a person, that is generated based on the image data captured by the capture device, may be modified prior to animation. The modification may be made to the model generated from image data that represents a target or a target's motion, including user gestures, in the physical space. For example, certain joints of a skeletal model may be readjusted or realigned. A model of a target may be modified by applying differential correction, magnetism principles, binary snapping, confining virtual movement to defined spaces, or the like.

Abstract: It may be desirable to apply corrective data to aspects of captured image or the user-performed gesture for display of a visual representation that corresponds to the corrective data. The captured motion may be any motion in the physical space that is captured by the capture device, such as a camera. Aspects of a skeletal or mesh model of a person, that is generated based on the image data captured by the capture device, may be modified prior to animation. The modification may be made to the model generated from image data that represents a target or a target's motion, including user gestures, in the physical space. For example, certain joints of a skeletal model may be readjusted or realigned. A model of a target may be modified by applying differential correction, magnetism principles, binary snapping, confining virtual movement to defined spaces, or the like.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. The image may then be analyzed to identify one or more targets within the scene. When a target is identified, vertices may be generated. A mesh model may then be created by drawing lines that may connect the vertices. Additionally, a depth value may also be calculated for each vertex. The depth values of the vertices may then be used to extrude the mesh model such that the mesh model may represent the target in the three-dimensional virtual world. A colorization scheme, a texture, lighting effects, or the like, may be also applied to the mesh model to convey the depth the virtual object may have in the virtual world.

Abstract: A capture device may capture a user's motion and a display device may display a model that maps to the user's motion, including gestures that are applicable for control. A user may be unfamiliar with a system that maps the user's motions or not know what gestures are applicable for an executing application. A user may not understand or know how to perform gestures that are applicable for the executing application. Providing visual feedback representing instructional gesture data to the user can teach the user how to properly gesture. The visual feedback may be provided in any number of suitable ways. For example, visual feedback may be provided via ghosted images, player avatars, or skeletal representations. The system can process prerecorded or live content for displaying visual feedback representing instructional gesture data. The feedback can portray the deltas between the user's actual position and the ideal gesture position.