Abstract: Embodiments are disclosed for multi-color light-diffuser devices capable of displaying different colors based on stack configurations of polymer dispersed liquid crystal (PDLC) diffuser components, base layers, and filter layers. In one embodiment, a multi-color light-diffuser device can include a base layer positioned underneath a filter layer and a PDLC diffuser component positioned above the filter layer. In another embodiment, a multi-color light-diffuser can include an additional PDLC diffuser component in between the base layer and filter layer. The filter layer can be a dichroic material, a neutral density filter, or other transparent or semi-transparent material. The base layer can be an opaque material, such as paper, holographic film, iridescent film, or a mirror.

Abstract: Machine-learning prompt enhancement techniques are described. In one or more examples, by generating training data from high-quality digital content meeting specific criteria, a generative artificial intelligence (AI) system is configurable to train an enhancement machine-learning model to capture features pertaining to particular tasks or scenarios. The trained enhancement machine-learning model can then extract enhancement features from inputs, enabling the formation of enhanced prompts that guide generative AI models to generate digital content as suitable for the particular tasks or scenarios.

Abstract: Techniques for machine-learning material data generation are described. In an example, a processing device is operable to receive a digital image depicting a material and generate a set of video frames depicting physically-based rendering properties of the material. The set of video frames is generated based on the digital image using a video generative machine-learning model. The processing device is operable to output material data based on the physically-based rendering properties of the material depicted by the set of video frames.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate and implement semantic histories for digital image editing. For instance, in some embodiments, the disclosed systems determine, for a digital image, a first semantic state and a second semantic state that reflects a first semantic change in the digital image from the first semantic state. Additionally, the disclosed systems determine, for the digital image, a third semantic state that reflects a second semantic change in the digital image from the first semantic state. The disclosed systems further generate a semantic history log for the digital image, the semantic history log including a first editing branch corresponding to the second semantic state and a second editing branch corresponding the third semantic state. Based on one or more user interactions with the semantic history log, the disclosed systems modify the digital image.

Abstract: Techniques for implementing 3D-aware transformations using a diffusion model are disclosed. A computing system receives an image of a three-dimensional (“3D”) object, a text prompt, and a 3D transformation. The computing system generates an initial state for a diffusion model. The computing system generates, using the diffusion model, a second image of the 3D object and intermediate representations of the second image, based on the initial state, a depth map, and the text prompt. The computing system generates 3D representations of the second image based on the intermediate representations, transformed 3D representations of the second image by applying the 3D transformation, and edited intermediate representations. The computing system generates, using the diffusion model and the edited intermediate representations, an edited image of the 3D object, based on the initial state, a second depth map, and the text prompt and outputs the edited image.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that trains (and utilizes) an image color editing diffusion neural network to generate a color edited digital image(s) for a digital image. In particular, in one or more implementations, the disclosed systems identify a digital image depicting content in a first color style. Moreover, the disclosed systems generate, from the digital image utilizing an image color editing diffusion neural network, a color-edited digital image depicting the content in a second color style different from the first color style. Further, the disclosed systems provide, for display within a graphical user interface, the color-edited digital image.

Abstract: A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining an outline image and an input prompt, where the outline image includes an outline of a region and the input prompt indicates content for the region, generating a mask based on the outline image, where the mask indicates the region within the outline, generating a preliminary image based on the mask, where the preliminary image includes noise in the region indicated by the outline, and generating a synthetic image based on the input prompt, the mask, and the preliminary image, where the synthetic image depicts the content in the region indicated by the outline. A vectorized image is generated based on the synthetic image.

Abstract: In implementation of techniques for editing digital images based on masks, a computing device implements an image combination system to receive a first digital image, a second digital image, a prompt describing an edit to the first digital image, and a mask identifying a portion of the second digital image. Using a machine learning model, the image combination system determines a feature of the portion of the second digital image identified by the mask to incorporate into content of the first digital image based on the prompt. The image combination system generates an edited digital image based on the edit described by the prompt, including the feature of the portion of the second digital image incorporated into the content of the first digital image. The image combination system then presents the edited digital image in a user interface.

Abstract: Digital image text editing techniques as implemented by an image processing system are described that support increased user interaction in the creation and editing of digital images through understanding a content creator's intent as expressed using text. In one example, a text user input is received by a text input module. The text user input describes a visual object and a visual attribute, in which the visual object specifies a visual context of the visual attribute. A feature representation generated by a text-to-feature system using a machine-learning module based on the text user input. The feature representation is passed to an image editing system to edit a digital object in a digital image, e.g., by applying a texture to an outline of the digital object within the digital image.

Abstract: Inpainting dispatch techniques for digital images are described. In one or more examples, an inpainting system includes a plurality of inpainting modules. The inpainting modules are configured to employ a variety of different techniques, respectively, as part of performing an inpainting operation. An inpainting dispatch module is also included as part of the inpainting system that is configured to select which of the plurality of inpainting modules are to be used to perform an inpainting operation for one or more regions in a digital image, automatically and without user intervention.

Abstract: Aspects of the method, apparatus, non-transitory computer readable medium, and system include obtaining a document and an information element. The aspects further include identifying, from the document, an anchor element that has an anchor type and a relationship type, wherein the anchor type describes a structure of a set of anchor elements, and the relationship type describes a relationship between the anchor element and the information element. The aspects further include extracting information corresponding to the information element based on the anchor element, the anchor type, and the relationship type, and displaying the extracted information to a user.

Abstract: In implementation of techniques for generating brushstrokes with gravity-based fluid flow, a computing device implements a fluid flow system to receive an input stroke on a virtual canvas. The fluid flow system determines a gravity feature involving simulated fluid interaction with the virtual canvas using an algorithm based on a geometry of the virtual canvas. Based on the gravity feature, the fluid flow system generates a brushstroke based on a shape of the input stroke and that simulates fluid interaction on the virtual canvas. The fluid flow system then presents the brushstroke on the virtual canvas in a user interface.

Abstract: In implementation of techniques for generating a re-illuminated reconstruction of an object, a computing device implements a reconstruction system to receive digital images depicting an object from different angles and a selection of an illumination direction for virtually illuminating the object. The reconstruction system determines a geometry of the object using a machine learning model based on the digital images. Based on the geometry of the object, the reconstruction system determines illumination parameters corresponding to the illumination direction. The reconstruction system renders a reconstructed virtual object that is a virtual representation of the object based on the geometry and illuminated based on the illumination parameters.

Abstract: In accordance with the described techniques, a query is received from a user for processing by an artificial intelligence (AI) chat agent. An ambiguity classifier model is leveraged to determine whether the query is ambiguous. Responsive to determining that the query is ambiguous, a query rewrite model is leveraged to update the query to resolve one or more ambiguities of the query, and the updated query is provided to the AI chat agent for processing. Responsive to determining that the query is unambiguous, the query is provided to the AI chat agent for processing.

Abstract: In implementations of techniques and systems for independent alternating-input (“IAI”) devices, a control system includes multiple IAI devices and a logic circuit component. The logic circuit component includes, for example, an analog switch, a d-type latch, a shift register, and an LED driver. The control system provides a first voltage signal (e.g., a bus signal) to a first voltage input of each IAI device. In response to a control signal from the control system, the logic circuit component provides a second voltage signal to a second voltage input of each IAI device. The second voltage signal can include the bus signal or an inverted bus signal. Each IAI device is activated in response to a voltage differential across the voltage inputs. In this way, the quantity of IAI devices for large arrangements (e.g., high-resolution display systems) can be greatly increased by utilizing low-cost logic circuits.

Abstract: Various disclosed embodiments are directed to automatically generating a score indicative of a recommendation for applying a color value to content in an image based on extracting features from one or more objects in the image. In other words, particular embodiments map currently detected objects in an image to objects of the same class of historical images, which are then mapped to their corresponding background colors in order to recommend a particular background color of an image. In an illustrative example, if a particular set of Christmas objects, such as a reindeer object and Santa object, have historically been included in a red background, particular embodiments recommend a red color for the background of a currently processed image that includes the same reindeer and Santa objects.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides to a user a subset of digital design templates as recommendations based on a creative segment classification and template classifications. For instance, in one or more embodiments, the disclosed systems generate the creative segment classification for the user and determines geo-seasonal intent data. Furthermore, the disclosed system generates template classifications using a machine learning model based on geo-seasonality and creative intent. In doing so, the disclosed system identifies a subset of digital design templates based on the template classifications, geo-seasonal intent data, and the creative segment classification of the user.

Abstract: Aspects and features of the present disclosure relate to providing injective three-dimensional (3D) deformations based on two-dimensional (2D) mesh deformations. For example, a method involves defining at least one 2D mesh deformation based on a designated position of an object represented by an input neural radiance field (NeRF). The method also involves applying the 2D mesh deformation(s) to a 3D piecewise-linear map that operates over a plane and preserves a normal direction to produce prismatic maps. The method further involves composing a 3D deformation for the object from layers defined by the prismatic maps, and parameterizing the 3D piecewise-linear map. The method additionally involves storing or rendering, using the 3D piecewise-linear map, a deformed NeRF injectively representing the object in the designated position. Aspects also include computer systems, apparatus, and computer programs configured to perform the method.

Abstract: Certain aspects and features of this disclosure relate to automatic generation of composite images. For example, a method involves producing a representative image corresponding to a composite image based on a presentation context of input objects and segmenting the generated objects from the representative image to extract the generated objects from the representative image. The method also includes generating an inferred disposition of each of the generated objects and transforming each of the input objects to the inferred disposition of a corresponding generated object. The method can also include transmitting, storing, display, or rendering, in response to the transforming, the composite image of the input objects. Certain aspects and features also include computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide a digital design interface for intuitively creating custom arrows that demonstrate both visual consistency and inherent directionality within vector-based design applications. In particular, in one or more implementations, the disclosed systems receive a request to create a custom arrow from a digital object and a path segment. In addition, the disclosed systems detect that the digital object is within a threshold distance of the path segment and combine the digital object with the path segment to create a custom arrow object. In particular, the disclosed systems utilize a bilateral segmentation machine-learning model to segment the digit object and a symmetry axis detection model to determine an axis of symmetry of the digital object. Moreover, the disclosed systems attach the digital object to an endpoint of the path segment at the axis of symmetry.