Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a transformer-based encoder-decoder neural network architecture for generating alpha mattes for digital images. Specifically, the disclosed system utilizes a transformer encoder to generate patch-based encodings from a digital image and a trimap segmentation by generating patch encodings for image patches and comparing the patch encodings to areas of the digital image. Additionally, the disclosed system generates modified patch-based encodings utilizing a plurality of neural network layers. The disclosed system also generates an alpha matte for the digital image from the patch-based encodings utilizing a decoder that includes a plurality of upsampling layers connected to a plurality of neural network layers via skip connections.

Abstract: The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for performing interactive digital image editing operations utilizing machine learning models and a feature backpropagation refinement layer. For example, the disclosed systems perform interactive digital image editing operations by incorporating a feature backpropagation refinement layer within a non-interactive machine learning model that utilizes a consistency loss to adjust the feature backpropagation refinement layer according to one or more user interactions. In some embodiments, the disclosed systems utilize a feature backpropagation refinement layer that includes a bias sublayer for localizing changes to a digital image and a convolutional sublayer for channel-wise scale and feature combinations across channels. In some cases, the disclosed systems utilize a consistency loss that facilitates localized modifications to a digital image based on distances of various pixels or features from a user interaction.

Abstract: This disclosure describes one or more implementations of an alpha matting system that utilizes a deep learning model to generate alpha mattes for digital images utilizing an alpha-range classifier function. More specifically, in various implementations, the alpha matting system builds and utilizes an object mask neural network having a decoder that includes an alpha-range classifier to determine classification probabilities for pixels of a digital image with respect to multiple alpha-range classifications. In addition, the alpha matting system can utilize a refinement model to generate the alpha matte from the pixel classification probabilities with respect to the multiple alpha-range classifications.

Abstract: The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

Abstract: Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

Abstract: A particulate material useful for additive manufacturing contains a semicrystalline polycarbonate or a semicrystalline polyetherimide. The particles of the particulate material are characterized by a narrow volume-based distribution of equivalent spherical diameters in which the median equivalent spherical diameter (Dv50) M is in the range 35 to 85 micrometers, the equivalent spherical diameter corresponding to 1 percent of the cumulative undersize distribution (DvO1) is greater than 2 micrometers, and the equivalent spherical diameter corresponding to 99 percent of the cumulative undersize distribution (Dv99) is less than 115 micrometers. Also described is a method of additive manufacturing utilizing the particulate material.

Abstract: The present invention relates to a method for producing a three-dimensional (3D) printed article with a photocurable silicone composition involving a silicone containing as end-group specific (meth)acrylate groups.

Abstract: The present invention relates to a method for producing a three-dimensional (3D) printed article with a photocurable silicone composition involving a silicone containing as end-group specific (meth)acrylate groups.

Abstract: A particulate material for powder bed fusion has specific particle size characteristics and includes a thermoplastic and a sulfonate salt having the structure (A), wherein Z is a phosphorus atom or a nitrogen atom; each occurrence of X is independently halogen or hydrogen provided that at least one X is halogen; b, d, and e are integers from zero to 12; c is 0 or 1 provided that when c is 1, d and e are not both zero; R11_13 are each independently C1-C12 hydrocarbyl; R14 is C1-C18 hydrocarbyl; and Y is selected from (B)—wherein R15 is hydrogen or C1-C12 hydrocarbyl. Also described is a method of powder bed fusion utilizing the particulate material.

Abstract: Described techniques for multi-label classification, in which sequential data includes characters that have two or more aspects that require classification, are capable of providing separate classifications for different categories of components. Using an appropriately-trained neural network, the described techniques perform aligning and otherwise combining two or more classifications (e.g., categories, or types of labels) to obtain multi-label characters.

Abstract: The present disclosure relates to a multi-model object segmentation system that provides a multi-model object segmentation framework for automatically segmenting objects in digital images. In one or more implementations, the multi-model object segmentation system utilizes different types of object segmentation models to determine a comprehensive set of object masks for a digital image. In various implementations, the multi-model object segmentation system further improves and refines object masks in the set of object masks utilizing specialized object segmentation models, which results in more improved accuracy and precision with respect to object selection within the digital image. Further, in some implementations, the multi-model object segmentation system generates object masks for portions of a digital image otherwise not captured by various object segmentation models.

Abstract: The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.

Abstract: A temporal object segmentation system determines a location of an object depicted in a video. In some cases, the temporal object segmentation system determines the object's location in a particular frame of the video based on information indicating a previous location of the object in a previous video frame. For example, an encoder neural network in the temporal object segmentation system extracts features describing image attributes of a video frame. A convolutional long-short term memory neural network determines the location of the object in the frame, based on the extracted image attributes and information indicating a previous location in a previous frame. A decoder neural network generates an image mask indicating the object's location in the frame. In some cases, a video editing system receives multiple generated masks for a video, and modifies one or more video frames based on the locations indicated by the masks.

Abstract: A particulate material useful for additive manufacturing contains a semicrystalline polycarbonate or a semicrystalline polyetherimide. The particles of the particulate material are characterized by a narrow volume-based distribution of equivalent spherical diameters in which the median equivalent spherical diameter (Dv50) M is in the range 35 to 85 micrometers, the equivalent spherical diameter corresponding to 1 percent of the cumulative undersize distribution (DvO1) is greater than 2 micrometers, and the equivalent spherical diameter corresponding to 99 percent of the cumulative undersize distribution (Dv99) is less than 115 micrometers. Also described is a method of additive manufacturing utilizing the particulate material.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing scale-diverse segmentation neural networks to analyze digital images at different scales and identify different target objects portrayed in the digital images. For example, in one or more embodiments, the disclosed systems analyze a digital image and corresponding user indicators (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) at different scales utilizing a scale-diverse segmentation neural network. In particular, the disclosed systems can utilize the scale-diverse segmentation neural network to generate a plurality of semantically meaningful object segmentation outputs. Furthermore, the disclosed systems can provide the plurality of object segmentation outputs for display and selection to improve the efficiency and accuracy of identifying target objects and modifying the digital image.

Abstract: A particulate material for powder bed fusion has specific particle size characteristics and includes a thermoplastic and a sulfonate salt having the structure (A), wherein Z is a phosphorus atom or a nitrogen atom; each occurrence of X is independently halogen or hydrogen provided that at least one X is halogen; b, d, and e are integers from zero to 12; c is 0 or 1 provided that when c is 1, d and e are not both zero; R11-13 are each independently C1-C12 hydrocarbyl; R14 is C1-C18 hydrocarbyl; and Y is selected from (B)—wherein R15 is hydrogen or C1-C12 hydrocarbyl. Also described is a method of powder bed fusion utilizing the particulate material.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a deep neural network to process object user indicators and an initial object segmentation from a digital image to efficiently and flexibly generate accurate object segmentations. In particular, the disclosed systems can determine an initial object segmentation for the digital image (e.g., utilizing an object segmentation model or interactive selection processes). In addition, the disclosed systems can identify an object user indicator for correcting the initial object segmentation and generate a distance map reflecting distances between pixels of the digital image and the object user indicator. The disclosed systems can generate an image-interaction-segmentation triplet by combining the digital image, the initial object segmentation, and the distance map.