Abstract: Systems and methods for enabling users to generate and utilize neural radiance field models can include obtaining user image data and training one or more neural radiance field models based on the user image data. The systems and methods can include obtaining user images based on a determination that the user images depicted objects of a particular object type. The trained neural radiance field models can then be utilized for view synthesis image generation of the particular user objects.

Abstract: Systems and methods for generating and providing outputs in a multi-device system can include leveraging environment-based prompt generation and generative model response generation to provide dynamic response generation and display. The systems and methods can obtain input data associated with one or more computing devices within an environment, can obtain environment data descriptive of the plurality of computing devices within the environment, and can generate a prompt based on the input data and environment data. The prompt can be processed with a generative model to generate a model-generated output. The model-generated output can then be transmitted to a particular computing device of the plurality of computing devices.

Abstract: Techniques for presenting a content item using text-to-image machine-learned models are presented. For example, a system can obtain user personalization data associated with a user and merchant assets data of a merchant. Additionally, the system can process the user personalization data and the merchant assets data with a text generation model to generate one or more model-generated terms. Moreover, the system can process the one or more model-generated terms with an image generation model to generate one or more model-generated images. Furthermore, the system can determine a content item based on the one or more model-generated images. Subsequently, the system can present, on a display of a user device of the user, a graphical user interface having the content item.

Abstract: Systems, devices, and corresponding non-transitory computer readable media are described in relation to layout detection in respect of individual configurable modular units of an assembly of configurable modular units, that can be arranged in a continuous shape through a set of one or more physical connections.

Abstract: Systems and methods for performing user data search can include obtaining a search query, processing the search query with one or more machine-learned planning model to generate one or more specialized queries and instructions to search one or more specific user datasets with the one or more specialized queries, and processing the obtained search results with a generative response model to generate a response to the search query. The systems and methods can search across local databases and server databases. An on-device generative response model can be utilized for device local search results, while a server generative response model may be utilized for server-based search results.

Abstract: Systems and methods for generating and providing outputs in a multi-device system can include leveraging environment-based prompt generation and generative model response generation to provide dynamic response generation and display. The systems and methods can obtain input data associated with one or more computing devices within an environment, can obtain environment data descriptive of the plurality of computing devices within the environment, and can generate a prompt based on the input data and environment data. The prompt can be processed with a generative model to generate a model-generated output. The model-generated output can then be transmitted to a particular computing device of the plurality of computing devices.

Abstract: Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating synthetic images depicting synthetic environments that can then be matched to a real world example. The systems and methods can include obtaining a search query, which can be utilized to generate a prompt input that can be processed by an image generation model to generate a plurality of model-generated images. A selection can then be received that selects a particular model-generated image to utilize to query a database.

Abstract: Systems and methods for enabling users to generate and utilize neural radiance field models can include obtaining user image data and training one or more neural radiance field models based on the user image data. The systems and methods can include obtaining user images based on a determination that the user images depicted objects of a particular object type. The trained neural radiance field models can then be utilized for view synthesis image generation of the particular user objects.

Abstract: Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. The systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. A selection can then be received that selects a particular model-generated database to utilize to query a database.

Abstract: Systems and methods for generating and providing outputs in a multi-device system can include leveraging environment-based prompt generation and generative model response generation to provide dynamic response generation and display. The systems and methods can obtain input data associated with one or more computing devices within an environment, can obtain environment data descriptive of the plurality of computing devices within the environment, and can generate a prompt based on the input data and environment data. The prompt can be processed with a generative model to generate a model-generated output. The model-generated output can then be transmitted to a particular computing device of the plurality of computing devices.

Abstract: Disclosed implementations relate to structures that support an on-demand navigational corpus. An example method involves receiving a navigation request from a client device pertaining to an intent, determining seed content associated with the navigation request, utilizing a large foundational model to create a web page incorporating the seed content, based on a navigation model, and the intent, and delivering the generated web page for presentation on the client device. The method enables efficient and personalized web page generation based on user intent, enhancing user experience and facilitating dynamic navigation using raw seed content.

Abstract: Techniques involve triggering a divided screen mode for a computing environment in response to an indication that a display aspect of the computing environment has changed. In a first portion of the divided screen mode, an interface for a main application is displayed; in a second portion of the divided screen mode, an interface for a companion application is displayed. In some implementations, prior to the interface for the companion application being displayed, an icon representing the companion application is displayed in the second portion; the user may point to, or click on, the icon to launch the companion application.

Abstract: Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. The systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. A selection can then be received that selects a particular model-generated database to utilize to query a database.

Abstract: Systems and methods for generating and utilizing artificial intelligence generated badges can include processing web information associated with a subject to determine particular qualities of the subject. The qualities can then be utilized to generate one or more badges. The badges can then be utilized for search result determination and display. The badges may be utilized for search result ranking and may be utilized to annotate search results in a search results interface.

Abstract: Systems and methods for generating and providing outputs in a multi-device system can include leveraging environment-based prompt generation and generative model response generation to provide dynamic response generation and display. The systems and methods can obtain input data associated with one or more computing devices within an environment, can obtain environment data descriptive of the plurality of computing devices within the environment, and can generate a prompt based on the input data and environment data. The prompt can be processed with a generative model to generate a model-generated output. The model-generated output can then be transmitted to a particular computing device of the plurality of computing devices.

Abstract: Techniques for presenting a content item using text-to-image machine-learned models are presented. For example, a system can obtain user personalization data associated with a user and merchant assets data of a merchant. Additionally, the system can process the user personalization data and the merchant assets data with a text generation model to generate one or more model-generated terms. Moreover, the system can process the one or more model-generated terms with an image generation model to generate one or more model-generated images. Furthermore, the system can determine a content item based on the one or more model-generated images. Subsequently, the system can present, on a display of a user device of the user, a graphical user interface having the content item.

Abstract: Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. The systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. A selection can then be received that selects a particular model-generated database to utilize to query a database.