Abstract: Disclosed herein are methods and systems for assigning pixels distance-cost values using a flood fill technique. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user, obtaining depth data associated with the video data, and selecting seed pixels for a flood fill at least in part by using the depth information. The process also includes performing the flood fill from the selected seed pixels. The flood fill assigns respective distance-cost values to pixels of the video data based on position-space cost values and color-space cost values. In some embodiments, the process also includes classifying pixels of the video data as foreground based at least in part on the assigned distance-cost values. In some other embodiments, the process also includes assigning pixels of the video data foreground-likelihood values based at least in part on the assigned distance-cost values.

Abstract: Methods and systems for real-time user extraction using deep learning networks. In one embodiment, user extraction comprises obtaining a given frame of color pixel data, checking whether a reset flag is cleared or set, and generating a trimap for the given frame. If the reset flag is cleared, generating the trimap comprises: obtaining a user-extraction contour based on a preceding frame; and generating the trimap based on the obtained user-extraction contour. If the reset flag is set, generating the trimap comprises: detecting at least one persona feature in the given frame; generating an alpha mask by aligning an intermediate contour with the detected persona feature(s), wherein the intermediate contour is based on a color-based flood-fill operation performed on a previous frame which was segmented by a machine-learning-segmentation process; and generating the trimap based on the generated alpha mask. The generated trimap is output for extracting a user persona.

Abstract: Disclosed herein are methods and systems for assigning pixels distance-cost values using a flood fill technique. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user, obtaining depth data associated with the video data, and selecting seed pixels for a flood fill at least in part by using the depth information. The process also includes performing the flood fill from the selected seed pixels. The flood fill assigns respective distance-cost values to pixels of the video data based on position-space cost values and color-space cost values. In some embodiments, the process also includes classifying pixels of the video data as foreground based at least in part on the assigned distance-cost values. In some other embodiments, the process also includes assigning pixels of the video data foreground-likelihood values based at least in part on the assigned distance-cost values.

Abstract: Disclosed herein are methods and systems for generating a user-hair-color model. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user. The process also includes determining a set of line segments of pixels of the video data, wherein each line segment in the determined set of line segments intersects an upper contour of the depicted head of the user. The process also includes grouping at least some of the pixels of at least one of the line segments in the determined set of line segments into three sets of pixels based at least in part on respective color data of the pixels. The three sets of pixels include a skin-pixel set, a hair-pixel set, and a background-pixel set. The process also includes updating a user hair-color model based at least in part on the skin-pixel set.

Abstract: Disclosed herein are methods and systems for generating a user-hair-color model. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user. The process also includes determining a set of line segments of pixels of the video data, wherein each line segment in the determined set of line segments intersects an upper contour of the depicted head of the user. The process also includes grouping at least some of the pixels of at least one of the line segments in the determined set of line segments into three sets of pixels based at least in part on respective color data of the pixels. The three sets of pixels include a skin-pixel set, a hair-pixel set, and a background-pixel set. The process also includes updating a user hair-color model based at least in part on the skin-pixel set.

Abstract: Disclosed herein are methods and systems for assigning pixels distance-cost values using a flood fill technique. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user, obtaining depth data associated with the video data, and selecting seed pixels for a flood fill at least in part by using the depth information. The process also includes performing the flood fill from the selected seed pixels. The flood fill assigns respective distance-cost values to pixels of the video data based on position-space cost values and color-space cost values. In some embodiments, the process also includes classifying pixels of the video data as foreground based at least in part on the assigned distance-cost values. In some other embodiments, the process also includes assigning pixels of the video data foreground-likelihood values based at least in part on the assigned distance-cost values.

Abstract: Disclosed herein are methods and systems for assigning pixels distance-cost values using a flood fill technique. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user, obtaining depth data associated with the video data, and selecting seed pixels for a flood fill at least in part by using the depth information. The process also includes performing the flood fill from the selected seed pixels. The flood fill assigns respective distance-cost values to pixels of the video data based on position-space cost values and color-space cost values. In some embodiments, the process also includes classifying pixels of the video data as foreground based at least in part on the assigned distance-cost values. In some other embodiments, the process also includes assigning pixels of the video data foreground-likelihood values based at least in part on the assigned distance-cost values.