Abstract: An illustrative content server system associated with a server domain may transmit, to a client device associated with a client domain, a data stream representing media content. The media content is represented at a first quality level for presentation within a presentation environment as the data stream is transmitted. Based on environment data captured in the client domain to represent real-time conditions of the presentation environment as the media content is presented, the content server system may determine an engagement parameter representing a real-time engagement level of a user with respect to the media content. Based on this engagement parameter, the content server system may adjust, in real time during the transmitting of the data stream and presentation of the media content, the data stream to represent the media content at a second quality level that is different from the first quality level. Corresponding methods and systems are also disclosed.

Abstract: An illustrative volumetric content production system may access first capture data and second capture data. These first capture data may represent an entirety of a capture target and may be captured by a fixed camera system including a first plurality of image capture devices having respective fixed viewpoints with respect to the capture target. The second capture data may represent a portion of the capture target less than the entirety of the capture target and may be captured by a dynamic camera system including a second plurality of image capture devices having respective dynamic viewpoints with respect to the capture target. Based on the first and second capture data, the volumetric content production system may generate a volumetric representation of an object included in the portion of the capture target in accordance with principles described herein. Corresponding methods and systems are also disclosed.

Abstract: An illustrative image processing system determines that an object depicted in an image is an instance of an object type for which a machine learning model is available to the image processing system. In response to the determining that the object is the instance of the object type, the image processing system obtains pose data generated based on the machine learning model to represent how objects of the object type are capable of being posed. The image processing system generates a volumetric representation of the object in an estimated pose. The estimated pose is estimated independently of depth data for the object based on the image and the pose data. Corresponding methods and systems are also disclosed.

Abstract: An illustrative audiovisual content rendering system generates a light dataset configured to model light energy at a particular location within a simulated 3D environment for a video frame time. The audiovisual content rendering system also generates an audio dataset configured to model acoustic energy at the particular location within the simulated 3D environment for an audio frame time concurrent with the video frame time. The audiovisual content rendering system stores the light dataset and the audio dataset together within a voxel-based data structure. More particularly, the light dataset and the audio dataset are stored together within a particular voxel of the voxel-based data structure that corresponds to the particular location within the simulated 3D environment. Corresponding methods and systems are also disclosed.

Abstract: An illustrative image processing system extracts a first color-field image from an original color image associated with a set of color-field components. The first color-field image is associated with a first subset of the set of color-field components. The image processing system also extracts a second color-field image from the original color image. The second color-field image is associated with a second subset of the set of color-field components that is different from the first subset. The image processing system detects a first set of features within the first color-field image and a second set of features within the second color-field image. At least one feature is detected within the first color-field image and included in the first set of features while not being detected within the second color-field image or included in the second set of features. Corresponding methods and systems are also disclosed.

Abstract: An illustrative scene capture system determines a set of two-dimensional (2D) feature pairs each representing a respective correspondence between particular features depicted in both a first intensity image from a first vantage point and a second intensity image from a second vantage point. Based on the set of 2D feature pairs, the system determines a set of candidate three-dimensional (3D) feature pairs for a first depth image from the first vantage point and a second depth image from the second vantage point. The system selects a subset of selected 3D feature pairs from the set of candidate 3D feature pairs in a manner configured to minimize an error associated with a transformation between the first depth image and the second depth image. Based on the subset of selected 3D feature pairs, the system manages calibration parameters for surface data capture devices that captured the intensity and depth images.

Abstract: An illustrative image processing system identifies an object depicted in an image captured by a camera. The image processing system locates, within the image, a set of calibration points corresponding to features of the object. Based on the set of calibration points, the image processing system performs a calibration operation with respect to the camera. Additionally, the image processing system generates model data based on the image. The model data is representative of a model of the object depicted in the image. Corresponding methods and systems are also disclosed.

Abstract: An illustrative image processing system determines that an object depicted in an image is an instance of an object type for which a machine learning model is available to the image processing system. In response to the determining that the object is the instance of the object type, the image processing system obtains pose data generated based on the machine learning model to represent how objects of the object type are capable of being posed. The image processing system generates a volumetric representation of the object in an estimated pose. The estimated pose is estimated independently of depth data for the object based on the image and the pose data. Corresponding methods and systems are also disclosed.

Abstract: An illustrative scene capture system determines a set of two-dimensional (2D) feature pairs each representing a respective correspondence between particular features depicted in both a first intensity image from a first vantage point and a second intensity image from a second vantage point. Based on the set of 2D feature pairs, the system determines a set of candidate three-dimensional (3D) feature pairs for a first depth image from the first vantage point and a second depth image from the second vantage point. The system selects a subset of selected 3D feature pairs from the set of candidate 3D feature pairs in a manner configured to minimize an error associated with a transformation between the first depth image and the second depth image. Based on the subset of selected 3D feature pairs, the system manages calibration parameters for surface data capture devices that captured the intensity and depth images.

Abstract: An illustrative image processing system determines calibration parameters for a set of cameras including a first camera configured to capture a scene from a first vantage point and a second camera configured to capture the scene from a second vantage point. The image processing system obtains pose data for an object included in the scene and depicted by first and second images captured, respectively, by the first and second cameras. The pose data is representative of how the object is capable of being posed. Based on the calibration parameters, the pose data, and the first and second images, the image processing system estimates a pose of the object in the scene independently of depth data for the object. The image processing system also generates model data of the scene that includes a volumetric representation of the object in the estimated pose. Corresponding methods and systems are also disclosed.

Abstract: An illustrative image processing system determines calibration parameters for a set of cameras including a first camera configured to capture a scene from a first vantage point and a second camera configured to capture the scene from a second vantage point. The image processing system obtains pose data for an object included in the scene and depicted by first and second images captured, respectively, by the first and second cameras. The pose data is representative of how the object is capable of being posed. Based on the calibration parameters, the pose data, and the first and second images, the image processing system estimates a pose of the object in the scene independently of depth data for the object. The image processing system also generates model data of the scene that includes a volumetric representation of the object in the estimated pose. Corresponding methods and systems are also disclosed.

Abstract: An illustrative image processing system identifies an object depicted in an image captured by a camera. The image processing system locates, within the image, a set of calibration points corresponding to features of the object. Based on the set of calibration points, the image processing system performs a calibration operation with respect to the camera. Additionally, the image processing system generates model data based on the image. The model data is representative of a model of the object depicted in the image. Corresponding methods and systems are also disclosed.