Abstract: Systems and methods are disclosed for using a synthetic world interface to model environments, sensors, and platforms, such as for computer vision sensor platform design. Digital models may be passed through a simulation service to generate synthetic experiment data. Systematic sweeps of parameters for various components of the sensor or platform design under test, under multiple environmental conditions, can facilitate time- and cost-efficient engineering efforts by revealing parameter sensitivities and environmental effects for multiple proposed configurations. Searches through the generated synthetic experimental data results can permit rapid identification of desirable design configuration candidates.

Abstract: Techniques for assigning a gesture dictionary in a gesture-based system to a user comprise capturing data representative of a user in a physical space. In a gesture-based system, gestures may control aspects of a computing environment or application, where the gestures may be derived from a user's position or movement in a physical space. In an example embodiment, the system may monitor a user's gestures and select a particular gesture dictionary in response to the manner in which the user performs the gestures. The gesture dictionary may be assigned in real time with respect to the capture of the data representative of a user's gesture. The system may generate calibration tests for assigning a gesture dictionary. The system may track the user during a set of short gesture calibration tests and assign the gesture dictionary based on a compilation of the data captured that represents the user's gestures.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: An virtual character such as an on-screen object, an avatar, an on-screen character, or the like may be animated using a live motion of a user and a pre-recorded motion. For example, a live motion of a user may be captured and a pre-recorded motion such as a pre-recorded artist generated motion, a pre-recorded motion of the user, and/or a programmatically controlled transformation may be received. The live motion may then be applied to a first portion of an the virtual character and the pre-recorded motion may be applied to a second portion of the virtual character such that the virtual character may be animated with a combination of the live and pre-recorded motions.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: Techniques for assigning a gesture dictionary in a gesture-based system to a user comprise capturing data representative of a user in a physical space. In a gesture-based system, gestures may control aspects of a computing environment or application, where the gestures may be derived from a user's position or movement in a physical space. In an example embodiment, the system may monitor a user's gestures and select a particular gesture dictionary in response to the manner in which the user performs the gestures. The gesture dictionary may be assigned in real time with respect to the capture of the data representative of a user's gesture. The system may generate calibration tests for assigning a gesture dictionary. The system may track the user during a set of short gesture calibration tests and assign the gesture dictionary based on a compilation of the data captured that represents the user's gestures.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: Techniques for assigning a gesture dictionary in a gesture-based system to a user comprise capturing data representative of a user in a physical space. In a gesture-based system, gestures may control aspects of a computing environment or application, where the gestures may be derived from a user's position or movement in a physical space. In an example embodiment, the system may monitor a user's gestures and select a particular gesture dictionary in response to the manner in which the user performs the gestures. The gesture dictionary may be assigned in real time with respect to the capture of the data representative of a user's gesture. The system may generate calibration tests for assigning a gesture dictionary. The system may track the user during a set of short gesture calibration tests and assign the gesture dictionary based on a compilation of the data captured that represents the user's gestures.

Abstract: A capture device can detect gestures made by a user. The gestures can be used to control a gesture unaware program.

Abstract: A depth image of a scene may be received, observed, or captured by a device. The depth image may include a human target that may have, for example, a portion thereof non-visible or occluded. For example, a user may be turned such that a body part may not be visible to the device, may have one or more body parts partially outside a field of view of the device, may have a body part or a portion of a body part behind another body part or object, or the like such that the human target associated with the user may also have a portion body part or a body part non-visible or occluded in the depth image. A position or location of the non-visible or occluded portion or body part of the human target associated with the user may then be estimated.

Abstract: A capture device can detect gestures made by a user. The gestures can be used to control a gesture unaware program.

Abstract: An virtual character such as an on-screen object, an avatar, an on-screen character, or the like may be animated using a live motion of a user and a pre-recorded motion. For example, a live motion of a user may be captured and a pre-recorded motion such as a pre-recorded artist generated motion, a pre-recorded motion of the user, and/or a programmatically controlled transformation may be received. The live motion may then be applied to a first portion of an the virtual character and the pre-recorded motion may be applied to a second portion of the virtual character such that the virtual character may be animated with a combination of the live and pre-recorded motions.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: An virtual character such as an on-screen object, an avatar, an on-screen character, or the like may be animated using a live motion of a user and a pre-recorded motion. For example, a live motion of a user may be captured and a pre-recorded motion such as a pre-recorded artist generated motion, a pre-recorded motion of the user, and/or a programmatically controlled transformation may be received. The live motion may then be applied to a first portion of an the virtual character and the pre-recorded motion may be applied to a second portion of the virtual character such that the virtual character may be animated with a combination of the live and pre-recorded motions.

Abstract: A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Abstract: Techniques for assigning a gesture dictionary in a gesture-based system to a user comprise capturing data representative of a user in a physical space. In a gesture-based system, gestures may control aspects of a computing environment or application, where the gestures may be derived from a user's position or movement in a physical space. In an example embodiment, the system may monitor a user's gestures and select a particular gesture dictionary in response to the manner in which the user performs the gestures. The gesture dictionary may be assigned in real time with respect to the capture of the data representative of a user's gesture. The system may generate calibration tests for assigning a gesture dictionary. The system may track the user during a set of short gesture calibration tests and assign the gesture dictionary based on a compilation of the data captured that represents the user's gestures.

Abstract: Techniques for assigning a gesture dictionary in a gesture-based system to a user comprise capturing data representative of a user in a physical space. In a gesture-based system, gestures may control aspects of a computing environment or application, where the gestures may be derived from a user's position or movement in a physical space. In an example embodiment, the system may monitor a user's gestures and select a particular gesture dictionary in response to the manner in which the user performs the gestures. The gesture dictionary may be assigned in real time with respect to the capture of the data representative of a user's gesture. The system may generate calibration tests for assigning a gesture dictionary. The system may track the user during a set of short gesture calibration tests and assign the gesture dictionary based on a compilation of the data captured that represents the user's gestures.

Abstract: Techniques for assigning a gesture dictionary in a gesture-based system to a user comprise capturing data representative of a user in a physical space. In a gesture-based system, gestures may control aspects of a computing environment or application, where the gestures may be derived from a user's position or movement in a physical space. In an example embodiment, the system may monitor a user's gestures and select a particular gesture dictionary in response to the manner in which the user performs the gestures. The gesture dictionary may be assigned in real time with respect to the capture of the data representative of a user's gesture. The system may generate calibration tests for assigning a gesture dictionary. The system may track the user during a set of short gesture calibration tests and assign the gesture dictionary based on a compilation of the data captured that represents the user's gestures.

Abstract: Architecture that facilitates management of a build process according to a level of trust of a build entity. The build process processes one or more build entities, each of which is associated with a level of trust. These associations are stored in a policy file that is run against the one or more entities at the start of the build process. The build process runs at a permission level that is representative of the lowest level of trust of the build entities. The levels of trust include at least trusted, semi-trusted, and untrusted levels. If the lowest level is untrusted, the build process fails, and the user is notified.

Abstract: A capture device can detect gestures made by a user. The gestures can be used to control a gesture unaware program.