Abstract: The present invention provides an aqueous polymer dispersion and an aqueous coating composition comprising such aqueous polymer dispersion and providing coatings with improved early water blister resistance as well as satisfactory water streaking resistance and good durability properties.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via multi-layered scene completion techniques facilitated by artificial intelligence. For instance, in some embodiments, the disclosed systems receive a digital image portraying a first object and a second object against a background, where the first object occludes a portion of the second object. Additionally, the disclosed systems pre-process the digital image to generate a first content fill for the portion of the second object occluded by the first object and a second content fill for a portion of the background occluded by the second object. After pre-processing, the disclosed systems detect one or more user interactions to move or delete the first object from the digital image. The disclosed systems further modify the digital image by moving or deleting the first object and exposing the first content fill for the portion of the second object.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of high-resolution images using guided upsampling during image inpainting. For instance, an image inpainting system can apply guided upsampling to an inpainted image result to enable generation of a high-resolution inpainting result from a lower-resolution image that has undergone inpainting. To allow for guided upsampling during image inpainting, one or more neural networks can be used. For instance, a low-resolution result neural network (e.g., comprised of an encoder and a decoder) and a high-resolution input neural network (e.g., comprised of an encoder and a decoder). The image inpainting system can use such networks to generate a high-resolution inpainting image result that fills the hole, region, and/or portion of the image.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”).

Abstract: One or more techniques for roaming optimization for non-standalone operation mode of a user equipment (UE) are disclosed.

Abstract: An electronic apparatus and a method for reducing the number of commands are provided. The electronic apparatus includes a central processor and a co-processor. The central processor generates a plurality of original register setting commands to set at least one bit of at least one register of the co-processor. The original register setting commands include a plurality of first original register setting commands, and a plurality of setting targets of the first original register setting commands have address continuity. The central processor merges the first original register setting commands to generate at least one merged register setting command. The central processor transmits the at least one merged register setting command to the co-processor.

Abstract: An electronic apparatus and a method for reducing the number of commands are provided. The electronic apparatus includes a central processor and a co-processor. The central processor generates a plurality of original register setting commands to set at least one bit of at least one register of the co-processor. The original register setting commands include a plurality of first original register setting commands, and a plurality of setting targets of the first original register setting commands have address continuity. The central processor merges the first original register setting commands to generate at least one merged register setting command. The central processor transmits the at least one merged register setting command to the co-processor.

Abstract: An electronic apparatus and a method for reducing the number of commands are provided. The electronic apparatus includes a central processor and a co-processor. The central processor generates a plurality of original register setting commands to set at least one bit of at least one register of the co-processor. The original register setting commands include a plurality of first original register setting commands, and a plurality of setting targets of the first original register setting commands have address continuity. The central processor merges the first original register setting commands to generate at least one merged register setting command. The central processor transmits the at least one merged register setting command to the co-processor.

Abstract: A model simplification method is provided. The model simplification method includes: constructing a surrounding body to surround a model, wherein the model includes at least one primitive and a plurality of vertexes; drawing the model and the surrounding body to at least one rendering surface by respectively taking each of the plurality of vertexes as an eye-position; determining whether the surrounding body drawn on the rendering surface by taking a current vertex of the plurality of vertexes as the eye-position is occluded to decide whether to mark the current vertex as an invisible vertex; and eliminating a current primitive from the model when all vertexes of the current primitive of the at least one primitive are marked as the invisible vertex.

Abstract: An electronic apparatus and a method for reducing the number of commands are provided. The electronic apparatus includes a central processor and a co-processor. The central processor generates a plurality of original register setting commands to set at least one bit of at least one register of the co-processor. The original register setting commands include a plurality of first original register setting commands, and a plurality of setting targets of the first original register setting commands have address continuity. The central processor merges the first original register setting commands to generate at least one merged register setting command. The central processor transmits the at least one merged register setting command to the co-processor.

Abstract: A model simplification method is provided. The model simplification method includes: constructing a surrounding body to surround a model, wherein the model includes at least one primitive and a plurality of vertexes; drawing the model and the surrounding body to at least one rendering surface by respectively taking each of the plurality of vertexes as an eye-position; determining whether the surrounding body drawn on the rendering surface by taking a current vertex of the plurality of vertexes as the eye-position is occluded to decide whether to mark the current vertex as an invisible vertex; and eliminating a current primitive from the model when all vertexes of the current primitive of the at least one primitive are marked as the invisible vertex.