Abstract: Techniques for automatically placing and manipulating virtual objects of augmented reality (AR) and mixed reality (MR) simulations are described. One technique includes obtaining an indication of at least one virtual object available for placing within a real-world environment during an AR simulation or a MR simulation. A first representation of the real-world environment is generated, based on a scan of the real-world environment. At least one second representation of the real-world environment is generated from the first representation. A match is determined between the at least one virtual object and at least one available space within the real-world environment, based at least in part on evaluating the at least one virtual object and the at least one second representation with a machine learning model(s). The at least one virtual object is rendered on a computing device, based on the match.

Abstract: An image processing system includes a computing platform having processing hardware, a display, and a system memory storing a software code. The processing hardware is configured to execute the software code to receive a three-dimensional (3D) digital model, surround the 3D digital model with multiple virtual cameras oriented toward the 3D digital model, and generate, using the virtual cameras, a multiple renders of the 3D digital model. The processing hardware is further configured to execute the software code to generate a UV texture coordinate space for a surface projection of the 3D digital model, and to transfer, using the multiple renders, lighting color values for each of multiple surface portions of the 3D digital model to the UV texture coordinate space.

Abstract: A system includes a hardware processor and a memory storing a software code including a predictive model. The hardware processor executes the software code to receive an input including an image having a pixel anomaly, and image data identifying the location of the pixel anomaly in the image. The software code uses the predictive model to extract a global feature map of a global image region of the image, the pixel anomaly being located within the global image region; to extract a local feature map of a local image region of the image, the pixel anomaly being located within the local image region and the local image region being smaller than the global image region; and to predict, based on the global feature map and the local feature map, a distraction level of the pixel anomaly within the image.

Abstract: Various embodiments disclosed herein provide techniques for performing incremental natural language understanding on a natural language understanding (NLU) system. The NLU system acquires a first audio speech segment associated with a user utterance. The NLU system converts the first audio speech segment into a first text segment. The NLU system determines a first intent based on a text string associated with the first text segment, wherein the text string represents a portion of the user utterance. The NLU system generates a first response based on the first intent prior to when the user utterance completes.

Abstract: A graphical user interface (“GUI”) personalization application automatically generates a GUI for a software application. In operation, the GUI personalization application tracks interactions between a user and a first GUI that is associated with the software application and a GUI framework. The GUI personalization application then computes a behavioral simplification for the first GUI based on the interactions and the GUI framework. Subsequently, the GUI personalization application modifies the first GUI based on the behavioral simplification to generate a second GUI for the user that is associated with the software application and the GUI framework. Advantageously, the GUI personalization application can more efficiently and reliably improve the quality of the overall user experience when interacting with the software application across a wider range of users relative to prior art approaches.

Abstract: In various embodiments, an interactive streaming application plays back a media title via a client device. In operation, while a client device plays back a first chunk of the media title, the interactive streaming application determines a first movement of an internet of things (“IoT”) device. The interactive streaming application then determines, from multiple target states, a target state that is associated with the first movement. Based on the target state, the interactive streaming application determines that a second chunk of the media title should be a next chunk of the media title that is streamed to the client device for playback. The interactive streaming application then causes the second chunk of the media title to be streamed to the client device. Advantageously, the interactive streaming application can automatically personalize the playback of the media title for a user based, at least in part, on movements of the IoT device.

Abstract: A content transport security system includes a computing platform having processing hardware and a memory storing software code and a database including one or more business rule(s). The processing hardware executes the software code to intercept a content file including a content asset, during a file transfer of the content file between a client device and a destination device, determine an authorization status of the destination device, and decrypt the content file, using a decryption key corresponding to an encryption key available to the client device. The processing hardware further executes the software code to search the content asset for a forensic identifier, assign a classification to the file transfer, based on the authorization status and a result of the searching, the classification being one of allowable, forbidden, or suspicious, and allow or block the file transfer to the destination device, based on the classification and the business rule(s).

Abstract: A system includes a hardware processor and a memory storing a software code including a predictive model. The hardware processor executes the software code to receive an input including an image having a pixel anomaly, and image data identifying the location of the pixel anomaly in the image. The software code uses the predictive model to extract a global feature map of a global image region of the image, the pixel anomaly being located within the global image region; to extract a local feature map of a local image region of the image, the pixel anomaly being located within the local image region and the local image region being smaller than the global image region; and to predict, based on the global feature map and the local feature map, a distraction level of the pixel anomaly within the image.

Abstract: According to one implementation, a system for performing automated salience assessment of pixel anomalies includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor is configured to execute the software code to analyze an image for a presence of a pixel anomaly in the image, obtain a salience criteria for the image when the analysis of the image detects the presence of the pixel anomaly, and classify the pixel anomaly as one of a salient anomaly or an innocuous anomaly based on the salience criteria for the image. The hardware processor is further configured to execute the software code to disregard the pixel anomaly when the pixel anomaly is classified as an innocuous anomaly, and to flag the pixel anomaly when the pixel anomaly is classified as a salient anomaly.

Abstract: A system includes a hardware processor and a memory storing a software code including a predictive model. The hardware processor executes the software code to receive an input including an image having a pixel anomaly, and image data identifying the location of the pixel anomaly in the image. The software code uses the predictive model to extract a global feature map of a global image region of the image, the pixel anomaly being located within the global image region; to extract a local feature map of a local image region of the image, the pixel anomaly being located within the local image region and the local image region being smaller than the global image region; and to predict, based on the global feature map and the local feature map, a distraction level of the pixel anomaly within the image.

Abstract: An image processing system includes a computing platform having processing hardware, a display, and a system memory storing a software code. The processing hardware is configured to execute the software code to receive a three-dimensional (3D) digital model, surround the 3D digital model with multiple virtual cameras oriented toward the 3D digital model, and generate, using the virtual cameras, a multiple renders of the 3D digital model. The processing hardware is further configured to execute the software code to generate a UV texture coordinate space for a surface projection of the 3D digital model, and to transfer, using the multiple renders, lighting color values for each of multiple surface portions of the 3D digital model to the UV texture coordinate space.

Abstract: A system includes a computing platform having processing hardware, and a system memory storing software code and one or more machine learning (ML) model(s) trained using contrastive learning based on a similarity metric. The processing hardware is configured to execute the software code to receive input data including a plurality of content segments, map, using the ML model(s), each of the plurality of content segments to a respective embedding in a continuous vector space to provide a plurality of mapped embeddings, and perform one of a classification or a regression of the content segments using the plurality of mapped embeddings. The processing hardware is also configured to execute the software code to discover, based on the classification or the regression, at least one new label for characterizing the plurality of content segments.

Abstract: A recommendation engine generates contextually relevant media programs tailored to the specific context of the user. The recommendation engine interfaces with one or more Internet-of-Things (IoT) appliances to determine one or more food items stored within the IoT appliance(s) and/or one or more cooking capabilities associated with the IoT appliance(s). The recommendation engine also gathers profile data that reflects a set of preferences associated with the user. Based on the gathered data, the recommendation engine queries a database of cooking program data associated with various cooking programs. The recommendation engine obtains cooking program data that is contextually relevant to the user and then generates a contextual cooking program that describes how to perform a given recipe. During playback of the contextual cooking program, the recommendation engine can modify the contextual cooking program based on the progress of the user in following the recipe.

Abstract: A system includes a hardware processor and a memory storing a software code including a predictive model. The hardware processor executes the software code to receive an input including an image having a pixel anomaly, and image data identifying the location of the pixel anomaly in the image. The software code uses the predictive model to extract a global feature map of a global image region of the image, the pixel anomaly being located within the global image region; to extract a local feature map of a local image region of the image, the pixel anomaly being located within the local image region and the local image region being smaller than the global image region; and to predict, based on the global feature map and the local feature map, a distraction level of the pixel anomaly within the image.

Abstract: A system includes processing hardware, a user interface (UI), and a system memory storing a software code. The processing hardware executes the software code to receive from a first user, via the UI, project data identifying a project including multiple tasks, determine, using the project data, a node-based graph of the project including the tasks, receive from a first user or a second user via the UI, task action data producing a modification to the node-based graph, and identify, based on the modification to the node-based graph, one of a conflict or a potential future conflict among at least two nodes of the node-based graph. The software code further identifies a conflict avoidance strategy for resolving the conflict or preventing the potential future conflict, and displays, via the UI, the conflict avoidance strategy to the first user or the second user, or performs the conflict avoidance strategy.

Abstract: A system includes processing hardware, and a memory storing software code including a hierarchical user interface (UI). The processing hardware executes the software code to receive from a first user, via the hierarchical UI, project data describing a project, and a project task for completion by a second user, and to generate, using the project data, a node-based graph of the project including the project task. The processing hardware further executes the software code to receive, via the hierarchical UI in response to performance of at least a portion of the project task by the second user, a task progression or task completion data, update, using the task progression or task completion data, the node-based graph, and display to the first user, via the hierarchical UI, the updated node-based graph and the task progression data or task completion data.

Abstract: According to one implementation, a system includes a computing platform having a hardware processor and a system memory storing a software code including a trained neural network (NN). The hardware processor executes the software code to receive an input image including a pixel anomaly, identify, using the trained NN, one or more salient regions of the input image, and determine whether the pixel anomaly is located inside any of the one or more salient regions. The hardware processor further executes the software code to assign a first priority to the pixel anomaly when it is determined that the pixel anomaly is located inside any of the one or more salient regions, and to assign a second priority, lower than the first priority, to the pixel anomaly when it is determined that the pixel anomaly is not located inside any of the one or more salient regions.

Abstract: In various embodiments, a storyboarding application generates a storyboard for a media title. In operation, the storyboarding application determines a categorization for a first portion of the media item. The storyboarding application then determines a first media item based on at least one of the categorization or a caption associated with the first portion of the media title. Subsequently, the storyboarding application modifies the first media item based on at least one of the categorization or a character associated with the caption to generate a second media item. The storyboarding application then generates a sequence of media items for the storyboard that includes the second media item. Advantageously, because the storyboarding application can automatically generate media items for storyboards based on categorizations and/or captions, the storyboarding application can reduce both the manual effort and time required to generate storyboards relative to prior art techniques.

Abstract: In various embodiments, an interactive streaming application plays back a media title via a client device. In operation, while a client device plays back a first chunk of the media title, the interactive streaming application determines a first movement of an internet of things (“IoT”) device. The interactive streaming application then determines, from multiple target states, a target state that is associated with the first movement. Based on the target state, the interactive streaming application determines that a second chunk of the media title should be a next chunk of the media title that is streamed to the client device for playback. The interactive streaming application then causes the second chunk of the media title to be streamed to the client device. Advantageously, the interactive streaming application can automatically personalize the playback of the media title for a user based, at least in part, on movements of the IoT device.

Abstract: A system performing guided user training includes a computing platform having a hardware processor and a memory storing a software code including a training recommendation engine. The hardware processor executes the software code to receive a login input from a user, identify, using the login input, a role type including the user, and determine, using the role type, one or more core competencies for the user. The software code further identifies, using the training recommendation engine, training resources for use in attaining the one or more core competencies, and generates, using the training recommendation engine and the user training profile, one or more learning playlists for attaining each of the core competencies using at least some of the identified training resources.