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 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: Methods and systems are disclosed for applying machine learning models to compressed videos. The system receives a video, depicting an object, that has previously been compressed using one or more video compression processes. The system analyzes, using one or more machine learning models, the video that has previously been compressed to generate a prediction corresponding to the object depicted in the video, with one or more artifacts resulting from application of the one or more machine learning models to the video that has been previously compressed being absent from the prediction. The system generates a visual output based on the prediction in which the one or more artifacts are absent.

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 an image depicting a real-world object and generates a prompt comprising a textual description of a fashion item. The system analyzes the image and the textual description of the fashion item using a generative machine learning model to generate an artificial image that depicts an artificial object that resembles the real-world object wearing an artificial fashion item matching the textual description of the fashion item. The system identifies an object comprising a real-world product image that matches visual attributes of the artificial fashion item and replaces the artificial fashion item in the artificial image with the object to generate an output image.

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 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 using machine learning models to perform smoothing in latent space using optical flow information. The methods and systems access a first frame of a video depicting an object and a second frame of the video, the second frame corresponding to a later time period in the video than the first frame. The methods and systems generate optical flow information based on the first frame and the second frame, the optical flow information describing movement of the object from the first frame to the second frame. The methods and systems smooth a latent space generated by one or more neural network encoders of a machine learning model using the optical flow information and process the smoothed latent space by one or more neural network decoders to generate a result of the machine learning model.

Abstract: Methods and systems are disclosed for applying machine learning models to compressed videos. The system receives a video, depicting an object, that has previously been compressed using one or more video compression processes. The system analyzes, using one or more machine learning models, the video that has previously been compressed to generate a prediction corresponding to the object depicted in the video, with one or more artifacts resulting from application of the one or more machine learning models to the video that has been previously compressed being absent from the prediction. The system generates a visual output based on the prediction in which the one or more artifacts are absent.

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: 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 an image depicting a real-world object and generates a prompt comprising a textual description of a fashion item. The system analyzes the image and the textual description of the fashion item using a generative machine learning model to generate an artificial image that depicts an artificial object that resembles the real-world object wearing an artificial fashion item matching the textual description of the fashion item. The system identifies an object comprising a real-world product image that matches visual attributes of the artificial fashion item and replaces the artificial fashion item in the artificial image with the object to generate an output image.

Abstract: Systems and methods are provided for generating an augmented reality (AR) experience. The systems and methods receive a video depicting movement of a humanoid and a target image depicting an object. The systems and methods process, by a generative machine learning (ML) model, the video and the target image to generate a new video depicting the object performing the movement. The systems and methods generate the AR experience using the new video to overlay a face of a user on a portion of the new video.

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 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 high resolution images using lower resolution machine learning models. The system receives one or more images depicting a real-world object in a real-world scene and receives a source image depicting a fashion item comprising a target. The system processes, using one or more machine learning models, the one or more images together with the source image to generate a new image depicting the real-world object wearing the fashion item depicted in the source image, the new image having a lower image resolution than an image resolution of the source image. The system selectively blends pixels of the new image with pixels of the source image to generate a virtual extended reality (XR) experience.

Abstract: Methods and systems are disclosed for applying machine learning models to compressed videos. The system receives a video, depicting an object, that has previously been compressed using one or more video compression processes. The system analyzes, using one or more machine learning models, the video that has previously been compressed to generate a prediction corresponding to the object depicted in the video, with one or more artifacts resulting from application of the one or more machine learning models to the video that has been previously compressed being absent from the prediction. The system generates a visual output based on the prediction in which the one or more artifacts are absent.

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: 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 an image depicting a real-world object and generates a prompt comprising a textual description of a fashion item. The system analyzes the image and the textual description of the fashion item using a generative machine learning model to generate an artificial image that depicts an artificial object that resembles the real-world object wearing an artificial fashion item matching the textual description of the fashion item. The system identifies an object comprising a real-world product image that matches visual attributes of the artificial fashion item and replaces the artificial fashion item in the artificial image with the object to generate an output image.