Abstract: Methods and systems are disclosed for using machine learning models to perform pixel-based deformation of fashion items using N-flows estimation. The methods and systems receive one or more images depicting a first person in a first pose and receive a source image depicting a target fashion item worn on a portion of a body of a second person in a second pose, the target fashion item comprising a plurality of parts. The methods and systems process, using one or more machine learning models, the one or more images together with the source image to generate a set of data comprising a plurality of flow fields each associated with a different one of the plurality of parts of the target fashion item. The methods and systems modify a portion of the one or more images to overlay the target fashion item on the first person in the first pose.

Abstract: Methods and systems are disclosed for using machine learning models to perform pixel-based deformation of fashion items. The methods and systems receive one or more images depicting a person in an individual pose and receive a first source image depicting a first view of a target fashion item and a second source image depicting a second view of the target fashion item. The methods and systems process, using one or more machine learning models, the one or more images that depict the person in the individual pose together with the first and second source images to generate a flow field, the flow field indicating a likelihood of existence and location of each pixel of the one or more images relative to the first and second source images. The methods and systems modify a portion of the one or more images to overlay the target fashion item on the person.

Abstract: Methods and systems are disclosed for generating 3D assets, such as for an extended reality (XR) experience. The system receives a plurality of textures associated with an object, each texture of the plurality of textures corresponding to a different view of the object and automatically generates an initial three-dimensional (3D) model of the object based on an initial alignment of the plurality of textures to respective portions of the 3D model. The system receives input that adjusts the initial alignment of the plurality of textures to the respective portions of the 3D model and combines the plurality of textures into a single texture based on the input, the single texture defining visual attributes of the object from multiple views. The system stores the 3D model in association with the single texture.

Abstract: Examples described herein relate to automatic image generation. A plurality of inputs is accessed. The inputs include first input data and second input data. The first input data includes a text prompt describing a desired image and the second input data is indicative of one or more structural features of the desired image. One or more intermediate outputs are generated via a first generative machine learning model that uses the plurality of inputs as first control signals. An output image is generated via a second generative machine learning model that uses at least a subset of the plurality of inputs and at least a subset of the one or more intermediate outputs as second control signals. The output image is presented at a user device of a user.

Abstract: Methods and systems are disclosed for using machine learning models to perform pixel-based deformation of fashion items. The methods and systems receive one or more images depicting a first person in a first pose and receive a source image depicting a target fashion item worn on a portion of a body of a second person in a second pose. The methods and systems process, using one or more machine learning models, the one or more images together with the source image to generate a flow field indicating existence and location of each pixel of the one or more images in the source image and modify, based on the flow field, a portion of the one or more images to overlay the target fashion item on the first person including one or more portions of the target fashion item that extend beyond a body of the first person.

Abstract: Methods and systems are disclosed for generating an extended reality (XR) try-on experience based on an image produced by a diffusion model. The system receives a first image depicting a real-world object and receives a second image depicting a target fashion item. The system generates a warped image in which pixels of the target fashion item depicted in the second image replace pixels of a portion of the real-world object in the first image and generates one or more segmentation maps corresponding to incomplete portions of the warped image. The system analyzes the warped image and the one or more segmentation maps using a generative machine learning model to generate an artificial image that populates the incomplete portions of the warped image to depict the real-world object wearing the target fashion item.

Abstract: A camera may include an image sensor configured to capture and measure light of a scene to be imaged. Control circuitry may be configured to control the image sensor to measure instances of the light captured in main exposure windows interspersed with test exposure windows. Timing of the main exposure windows may be adjusted based on luminance levels captured during the test exposure windows, for synchronization with a flickering waveform of the light.

Abstract: A camera may include an image sensor configured to capture and measure light of a scene to be imaged. Control circuitry may be configured to control the image sensor to measure instances of the light captured in main exposure windows interspersed with test exposure windows. Timing of the main exposure windows may be adjusted based on luminance levels captured during the test exposure windows, for synchronization with a flickering waveform of the light.