Abstract: Generative shape creation and editing is leveraged in a digital medium environment. An object editor system represents a set of training shapes as sets of visual elements known as “handles,” and converts sets of handles into signed distance field (SDF) representations. A handle processor model is then trained using the SDF representations to enable the handle processor model to generate new shapes that reflect salient visual features of the training shapes. The trained handle processor model, for instance, generates new sets of handles based on salient visual features learned from the training handle set. Thus, utilizing the described techniques, accurate characterizations of a set of shapes can be learned and used to generate new shapes. Further, generated shapes can be edited and transformed in different ways.

Abstract: Various embodiments describe frame selection based on training and using a neural network. In an example, the neural network is a convolutional neural network trained with training pairs. Each training pair includes two training frames from a frame collection. The loss function relies on the estimated quality difference between the two training frames. Further, the definition of the loss function varies based on the actual quality difference between these two frames. In a further example, the neural network is trained by incorporating facial heatmaps generated from the training frames and facial quality scores of faces detected in the training frames. In addition, the training involves using a feature mean that represents an average of the features of the training frames belonging to the same frame collection. Once the neural network is trained, a frame collection is input thereto and a frame is selected based on generated quality scores.

Abstract: Embodiments of the present invention are directed to facilitating region of interest preservation. In accordance with some embodiments of the present invention, a region of interest preservation score using adaptive margins is determined. The region of interest preservation score indicates an extent to which at least one region of interest is preserved in a candidate image crop associated with an image. A region of interest positioning score is determined that indicates an extent to which a position of the at least one region of interest is preserved in the candidate image crop associated with the image. The region of interest preservation score and/or the preserving score are used to select a set of one or more candidate image crops as image crop suggestions.

Abstract: Embodiments of the present invention are directed towards intuitive editing of three-dimensional models. In embodiments, salient geometric features associated with a three-dimensional model defining an object are identified. Thereafter, feature attributes associated with the salient geometric features are identified. A feature set including a plurality of salient geometric features related to one another is generated based on the determined feature attributes (e.g., properties, relationships, distances). An editing handle can then be generated and displayed for the feature set enabling each of the salient geometric features within the feature set to be edited in accordance with a manipulation of the editing handle. The editing handle can be displayed in association with one of the salient geometric features of the feature set.

Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that can guide a user to align a camera feed captured by a user client device with a target digital image. In particular, the systems described herein can analyze a camera feed to determine image attributes for the camera feed. The systems can compare the image attributes of the camera feed with corresponding target image attributes of a target digital image. Additionally, the systems can generate and provide instructions to guide a user to align the image attributes of the camera feed with the target image attributes of the target digital image.

Abstract: Techniques are disclosed for deforming a 3D source mesh to resemble a target object representation which may be a 2D image or another 3D mesh. A methodology implementing the techniques according to an embodiment includes extracting a set of one or more source features from a source 3D mesh. The source 3D mesh includes a plurality of source points representing a source object, and the extracting of the set of source features is independent of an ordering of the source points. The method also includes extracting a set of one or more target features from the target object representation, and decoding a concatenation of the set of source features and the set of target features to predict vertex offsets for application to the source 3D mesh to generate a deformed 3D mesh based on the target object. The feature extractions and the vertex offset predictions may employ Deep Neural Networks.

Abstract: Systems and methods are disclosed for estimating aesthetic quality of digital images using deep learning. In particular, the disclosed systems and methods describe training a neural network to generate an aesthetic quality score digital images. In particular, the neural network includes a training structure that compares relative rankings of pairs of training images to accurately predict a relative ranking of a digital image. Additionally, in training the neural network, an image rating system can utilize content-aware and user-aware sampling techniques to identify pairs of training images that have similar content and/or that have been rated by the same or different users. Using content-aware and user-aware sampling techniques, the neural network can be trained to accurately predict aesthetic quality ratings that reflect subjective opinions of most users as well as provide aesthetic scores for digital images that represent the wide spectrum of aesthetic preferences of various users.

Abstract: Embodiments of the present invention are directed to facilitating region of interest preservation. In accordance with some embodiments of the present invention, a region of interest preservation score using adaptive margins is determined. The region of interest preservation score indicates an extent to which at least one region of interest is preserved in a candidate image crop associated with an image. A region of interest positioning score is determined that indicates an extent to which a position of the at least one region of interest is preserved in the candidate image crop associated with the image. The region of interest preservation score and/or the preserving score are used to select a set of one or more candidate image crops as image crop suggestions.

Abstract: Various methods and systems are provided for image-management operations that includes generating adaptive image armatures based on an alignment between composition lines of a reference armature and a position of an object in an image. In operation, a reference armature for an image is accessed. The reference armature includes a plurality of composition lines that define a frame of reference for image composition. An alignment map is determined using the reference armature. The alignment map includes alignment information that indicates alignment between the composition lines of the reference armature and the position of the object in the image. Based on the alignment map, an adaptive image armature is determined. The adaptive image armature includes a subset of the composition lines of the reference armature. The adaptive image armature is displayed.

Abstract: Digital image defect identification and correction techniques are described. In one example, a digital medium environment is configured to identify and correct a digital image defect through identification of a defect in a digital image using machine learning. The identification includes generating a plurality of defect type scores using a plurality of defect type identification models, as part of machine learning, for a plurality of different defect types and determining the digital image includes the defect based on the generated plurality of defect type scores. A correction is generated for the identified defect and the digital image is output as included the generated correction.

Abstract: Image search techniques and systems involving emotions are described. In one or more implementations, a digital medium environment of a content sharing service is described for image search result configuration and control based on a search request that indicates an emotion. The search request is received that includes one or more keywords and specifies an emotion. Images are located that are available for licensing by matching one or more tags associated with the image with the one or more keywords and as corresponding to the emotion. The emotion of the images is identified using one or more models that are trained using machine learning based at least in part on training images having tagged emotions. Output is controlled of a search result having one or more representations of the images that are selectable to license respective images from the content sharing service.

Abstract: Techniques are disclosed for blur classification. The techniques utilize an image content feature map, a blur map, and an attention map, thereby combining low-level blur estimation with a high-level understanding of important image content in order to perform blur classification. The techniques allow for programmatically determining if blur exists in an image, and determining what type of blur it is (e.g., high blur, low blur, middle or neutral blur, or no blur). According to one example embodiment, if blur is detected, an estimate of spatially-varying blur amounts is performed and blur desirability is categorized in terms of image quality.

Abstract: Various embodiments enable curves to be drawn around 3-D objects by intelligently determining or inferring how the curve flows in the space around the outside of the 3-D object. The various embodiments enable such curves to be drawn without having to constantly rotate the 3-D object. In at least some embodiments, curve flow is inferred by employing a vertex position discovery process, a path discovery process, and a final curve construction process.

Abstract: Various embodiments enable curves to be drawn around 3-D objects by intelligently determining or inferring how the curve flows in the space around the outside of the 3-D object. The various embodiments enable such curves to be drawn without having to constantly rotate the 3-D object. In at least some embodiments, curve flow is inferred by employing a vertex position discovery process, a path discovery process, and a final curve construction process.

Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that can guide a user to align a camera feed captured by a user client device with a target digital image. In particular, the systems described herein can analyze a camera feed to determine image attributes for the camera feed. The systems can compare the image attributes of the camera feed with corresponding target image attributes of a target digital image. Additionally, the systems can generate and provide instructions to guide a user to align the image attributes of the camera feed with the target image attributes of the target digital image.

Abstract: Techniques are disclosed for deforming a 3D source mesh to resemble a target object representation which may be a 2D image or another 3D mesh. A methodology implementing the techniques according to an embodiment includes extracting a set of one or more source features from a source 3D mesh. The source 3D mesh includes a plurality of source points representing a source object, and the extracting of the set of source features is independent of an ordering of the source points. The method also includes extracting a set of one or more target features from the target object representation, and decoding a concatenation of the set of source features and the set of target features to predict vertex offsets for application to the source 3D mesh to generate a deformed 3D mesh based on the target object. The feature extractions and the vertex offset predictions may employ Deep Neural Networks.

Abstract: Embodiments of the present invention are directed towards intuitive editing of three-dimensional models. In embodiments, salient geometric features associated with a three-dimensional model defining an object are identified. Thereafter, feature attributes associated with the salient geometric features are identified. A feature set including a plurality of salient geometric features related to one another is generated based on the determined feature attributes (e.g., properties, relationships, distances). An editing handle can then be generated and displayed for the feature set enabling each of the salient geometric features within the feature set to be edited in accordance with a manipulation of the editing handle. The editing handle can be displayed in association with one of the salient geometric features of the feature set.

Abstract: Systems and methods are disclosed for estimating aesthetic quality of digital images using deep learning. In particular, the disclosed systems and methods describe training a neural network to generate an aesthetic quality score digital images. In particular, the neural network includes a training structure that compares relative rankings of pairs of training images to accurately predict a relative ranking of a digital image. Additionally, in training the neural network, an image rating system can utilize content-aware and user-aware sampling techniques to identify pairs of training images that have similar content and/or that have been rated by the same or different users. Using content-aware and user-aware sampling techniques, the neural network can be trained to accurately predict aesthetic quality ratings that reflect subjective opinions of most users as well as provide aesthetic scores for digital images that represent the wide spectrum of aesthetic preferences of various users.

Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that can guide a user to align a camera feed captured by a user client device with a target digital image. In particular, the systems described herein can analyze a camera feed to determine image attributes for the camera feed. The systems can compare the image attributes of the camera feed with corresponding target image attributes of a target digital image. Additionally, the systems can generate and provide instructions to guide a user to align the image attributes of the camera feed with the target image attributes of the target digital image.

Abstract: In embodiments of event image curation, a computing device includes memory that stores a collection of digital images associated with a type of event, such as a digital photo album of digital photos associated with the event, or a video of image frames and the video is associated with the event. A curation application implements a convolutional neural network, which receives the digital images and a designation of the type of event. The convolutional neural network can then determine an importance rating of each digital image within the collection of the digital images based on the type of the event. The importance rating of a digital image is representative of an importance of the digital image to a person in context of the type of the event. The convolutional neural network generates an output of representative digital images from the collection based on the importance rating of each digital image.