Abstract: Disclosed are systems and methods that provide a computerized framework for automatically and/or dynamically generating video highlights via implementations of LLMs. In some implementations, the framework can leverage LLMs and contextual embeddings to automatically generate video highlights by identifying key moments, insights and/or salient points, determining a similarity among segments within the video, then compiling a concise highlights reel that can be curated and/or modified to a requesting and/or viewing user. Such automated highlight generation can save time, identify relevant content and deliver a customized video experience for each viewer.

Abstract: A system described herein may receive a first set of artificial intelligence/machine learning (“AI/ML”) models; receive first locally captured video information; generate a second set of AI/ML models based on the first set of AI/ML models and the locally captured video information; receive second locally captured video information; identify, based on the second locally captured video information and the second set of AI/ML models, one or more classifications for the second locally captured video information; and output, via a network and to an action system, the one or more classifications, without outputting the second locally captured video information via the network, wherein the action system identifies a particular action associated with the one or more classifications, and performs the identified particular action.

Abstract: An illustrative point cloud segmentation system generates an instance-wise semantic mask for a particular source image of a set of source images that has been used to construct a 3D point cloud representing a scene that includes one or more objects. The point cloud segmentation system maps a set of 3D points from the 3D point cloud to corresponding 2D points of the particular source image, then labels, based on contours defined by the instance-wise semantic mask to demarcate the one or more objects, the mapped set of 3D points in accordance with where the corresponding 2D point for each mapped 3D point is positioned with respect to the contours. Based on the labeling of the mapped 3D points, the point cloud segmentation system produces a segmented 3D point cloud including an instance-wise segmentation of the one or more objects at the scene. Corresponding methods and systems are also disclosed.

Abstract: A first capture engine application may synchronize a first clock to a global time domain to which a second clock used by a second capture engine application is also synchronized. The first capture engine application may receive a first image frame having a camera timestamp indicating a capture time of the first image frame with respect to a camera time domain. Based on the camera timestamp and the synchronized first clock, the first capture engine application may annotate the first image frame with a global timestamp indicating the capture time of the first image frame with respect to the global time domain. The first capture engine application may then transmit the annotated first image frame at a same transmission time as the second capture engine application also synchronously transmits a second image frame that is also annotated with the global timestamp. Corresponding methods and systems are also disclosed.

Abstract: An illustrative camera calibration system accesses a set of images captured by one or more cameras at a scene. The set of images may each depict a same element of image content present at the scene, but may depict the element of image content differently in each image so as to show an apparent movement of the element from one image to another. The camera calibration system applies a structure-from-motion algorithm to the set of images to generate calibration parameters for the one or more cameras based on the apparent movement of the element of image content shown in the set of images. Additionally, the camera calibration system provides the calibration parameters for the one or more cameras to a 3D modeling system configured to model the scene based on the set of images. Corresponding methods and systems are also disclosed.

Abstract: A device may receive video data and corresponding GPS data and IMU data associated with a vehicle, and may remove video frames from the video data to generate modified video data. The device may select objects and image regions of video frames of the modified video data, and may determine a current speed and a current turn angle of the vehicle based on the GPS data, the IMU data, and the modified video data. The device may mask the objects of the video frames of the modified video data to learn first features, and may mask the image regions of the video frames of the modified video data to learn second features. The device may generate a trained neural network model based on the current speed, the current turn angle, the first features, and the second features, and may implement the trained neural network model in the vehicle.

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: A device may receive video data and corresponding GPS data and IMU data associated with a vehicle, and may remove video frames from the video data to generate modified video data. The device may select objects and image regions of video frames of the modified video data, and may determine a current speed and a current turn angle of the vehicle based on the GPS data, the IMU data, and the modified video data. The device may mask the objects of the video frames of the modified video data to learn first features, and may mask the image regions of the video frames of the modified video data to learn second features. The device may generate a trained neural network model based on the current speed, the current turn angle, the first features, and the second features, and may implement the trained neural network model in the vehicle.

Abstract: In some implementations, a device may receive a multimedia content file. The device may divide the multimedia content file into a set of logical entities. The device may generate a set of embeddings for each logical entity of the set of logical entities. The device may compare groups of logical entities, of the set of logical entities, to generate a similarity metric. The device may selectively merge, based on the similarity metric satisfying a threshold, a pair of logical entities, in a group of logical entities of the groups of logical entities, to generate one or more logical entity sets. The device may process the one or more logical entity sets to generate one or more metadata tags for the one or more logical entity sets. The device may store a metadata file including the one or more metadata tags.

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: A device may receive video data and corresponding GPS data and IMU data associated with a vehicle, and may remove video frames from the video data to generate modified video data. The device may select objects and image regions of video frames of the modified video data, and may determine a current speed and a current turn angle of the vehicle based on the GPS data, the IMU data, and the modified video data. The device may mask the objects of the video frames of the modified video data to learn first features, and may mask the image regions of the video frames of the modified video data to learn second features. The device may generate a trained neural network model based on the current speed, the current turn angle, the first features, and the second features, and may implement the trained neural network model in the vehicle.

Abstract: An illustrative point cloud segmentation system generates an instance-wise semantic mask for a particular source image of a set of source images that has been used to construct a 3D point cloud representing a scene that includes one or more objects. The point cloud segmentation system maps a set of 3D points from the 3D point cloud to corresponding 2D points of the particular source image, then labels, based on contours defined by the instance-wise semantic mask to demarcate the one or more objects, the mapped set of 3D points in accordance with where the corresponding 2D point for each mapped 3D point is positioned with respect to the contours. Based on the labeling of the mapped 3D points, the point cloud segmentation system produces a segmented 3D point cloud including an instance-wise segmentation of the one or more objects at the scene. Corresponding methods and systems are also disclosed.

Abstract: An illustrative point cloud compression system accesses an input point cloud dataset representative of a point cloud comprising a plurality of points. The point cloud compression system identifies a first attribute dataset and a second attribute dataset within the input point cloud dataset. Based on an application of a transform algorithm to the first and second attribute datasets, respectively, the point cloud compression system generates 1) a first low-frequency component and a first high-frequency component of the first attribute dataset, and 2) a second low-frequency component and a second high-frequency component of the second attribute dataset. The point cloud compression system then generates an output point cloud dataset that prioritizes both the first and second low-frequency components above both the first and second high-frequency components. Corresponding methods and systems are also disclosed.

Abstract: A first capture engine application may synchronize a first clock to a global time domain to which a second clock used by a second capture engine application is also synchronized. The first capture engine application may receive a first image frame having a camera timestamp indicating a capture time of the first image frame with respect to a camera time domain. Based on the camera timestamp and the synchronized first clock, the first capture engine application may annotate the first image frame with a global timestamp indicating the capture time of the first image frame with respect to the global time domain. The first capture engine application may then transmit the annotated first image frame at a same transmission time as the second capture engine application also synchronously transmits a second image frame that is also annotated with the global timestamp. 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 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 3D modeling system generates a first voxelized representation of an object with respect to a voxel space and a second voxelized representation of the object with respect to the voxel space. Based on a first normal of a first voxel included in the first voxelized representation and a second normal of a second voxel included in the second voxelized representation, the 3D modeling system identifies a mergeable intersection between the first and second voxels. Based on the first and second voxelized representations, the 3D modeling system generates a merged voxelized representation of the object with respect to the voxel space. The merged voxelized representation includes a single merged voxel generated, based on the identified mergeable intersection, to represent both the first and second voxels. Corresponding methods and systems are also disclosed.

Abstract: An illustrative camera calibration system accesses a set of images captured by one or more cameras at a scene. The set of images may each depict a same element of image content present at the scene, but may depict the element of image content differently in each image so as to show an apparent movement of the element from one image to another. The camera calibration system applies a structure-from-motion algorithm to the set of images to generate calibration parameters for the one or more cameras based on the apparent movement of the element of image content shown in the set of images. Additionally, the camera calibration system provides the calibration parameters for the one or more cameras to a 3D modeling system configured to model the scene based on the set of images. Corresponding methods and systems are also disclosed.

Abstract: An illustrative content augmentation system identifies a presentation context dataset indicating how an extended reality (XR) presentation device is to present XR content in connection with a presentation of primary content. Based on the presentation context dataset, the content augmentation system selects a subset of secondary content items from a set of secondary content items each configured for presentation as XR content that augments the presentation of the primary content. The content augmentation system also provides the selected subset of secondary content items for presentation by the XR presentation device in connection with the presentation of the primary content. Corresponding methods and systems are also disclosed.

Abstract: An illustrative point cloud compression system accesses an input point cloud dataset representative of a point cloud comprising a plurality of points. The point cloud compression system identifies a first attribute dataset and a second attribute dataset within the input point cloud dataset. Based on an application of a transform algorithm to the first and second attribute datasets, respectively, the point cloud compression system generates 1) a first low-frequency component and a first high-frequency component of the first attribute dataset, and 2) a second low-frequency component and a second high-frequency component of the second attribute dataset. The point cloud compression system then generates an output point cloud dataset that prioritizes both the first and second low-frequency components above both the first and second high-frequency components. Corresponding methods and systems are also disclosed.