Abstract: A cached information updating method includes receiving an update request, determining an update processing manner according to a number of pieces of cached information to be updated indicated in the update request, and updating the cached information according to the update processing manner, to update differently according to different numbers of pieces of cached information.

Abstract: A vectorized caricature avatar generator receives a user image from which face parameters are generated. Segments of the user image including certain facial features (e.g., hair, facial hair, eyeglasses) are also identified. Segment parameter values are also determined, the segment parameter values being those parameter values from a set of caricature avatars that correspond to the segments of the user image. The face parameter values and the segment parameter values are used to generate a caricature avatar of the user in the user image.

Abstract: An expansion anchor includes an anchor bolt and an expansion sleeve which encompasses the anchor bolt. The anchor bolt has an expansion-sleeve expansion region and an expansion-element expansion region. The expansion sleeve has an expansion element which is disposed on the expansion sleeve. The expansion sleeve is radially displaceable by the expansion-sleeve expansion region and the expansion element is radially displaceable by the expansion-element expansion region. The expansion element is disposed on the expansion sleeve on a side of the expansion sleeve that radially faces the anchor bolt.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize image-guided model inversion of an image classifier with a discriminator. The disclosed systems utilize a neural network image classifier to encode features of an initial image and a target image. The disclosed system also reduces a feature distance between the features of the initial image and the features of the target image at a plurality of layers of the neural network image classifier by utilizing a feature distance regularizer. Additionally, the disclosed system reduces a patch difference between image patches of the initial image and image patches of the target image by utilizing a patch-based discriminator with a patch consistency regularizer. The disclosed system then generates a synthesized digital image based on the constrained feature set and constrained image patches of the initial image.

Abstract: Techniques for generating style-transferred images are provided. In some embodiments, a content image, a style image, and a user input indicating one or more modifications that operate on style-transferred images are received. In some embodiments, an initial style-transferred image is generated using a machine learning model. In some examples, the initial style-transferred image comprises features associated with the style image applied to content included in the content image. In some embodiments, a modified style-transferred image is generated by modifying the initial style-transferred image based at least in part on the user input indicating the one or more modifications.

Abstract: An image inpainting system is described that receives an input image that includes a masked region. From the input image, the image inpainting system generates a synthesized image that depicts an object in the masked region by selecting a first code that represents a known factor characterizing a visual appearance of the object and a second code that represents an unknown factor characterizing the visual appearance of the object apart from the known factor in latent space. The input image, the first code, and the second code are provided as input to a generative adversarial network that is trained to generate the synthesized image using contrastive losses. Different synthesized images are generated from the same input image using different combinations of first and second codes, and the synthesized images are output for display.

Abstract: The present disclosure provides a vector graphics data processing method, system, medium, and vector graphics processing device. The method includes the following operations: building a vector primitive path intersection data structure (PIDS) based on coordinates of path intersections (PIs); when a new PI is generated, comparing information of the new PI to information of existing PIs corresponding to an X coordinate or Y coordinate of the new PI; and storing the information of the new PI at a corresponding position in the PIDS corresponding to the X coordinate or Y coordinate of the new PI based on a result of the comparing. Only effective PI data are saved, thereby reducing memory footprint and memory bandwidth, and improving vector graphics processing performance.

Abstract: Techniques for identity preserved controllable facial image manipulation are described that support generation of a manipulated digital image based on a facial image and a render image. For instance, a facial image depicting a facial representation of an individual is received as input. A feature space including an identity parameter and at least one other visual parameter is extracted from the facial image. An editing module edits one or more of the visual parameters and preserves the identity parameter. A renderer generates a render image depicting a morphable model reconstruction of the facial image based on the edit. The render image and facial image are encoded, and a generator of a neural network is implemented to generate a manipulated digital image based on the encoded facial image and the encoded render image.

Abstract: An expansion anchor includes an anchor bolt and an expansion sleeve which surrounds the anchor bolt. The expansion sleeve has an expansion tongue and an anchor tongue and the anchor bolt has a backward-pointing expansion slope for radially displacing the expansion tongue and a backward-pointing anchor slope for radially displacing the anchor tongue. The backward-pointing anchor slope extends more steeply than the backward-pointing expansion slope at least in some regions.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images via multi-resolution generator neural networks. The disclosed system extracts multi-resolution features from a scene representation to condition a spatial feature tensor and a latent code to modulate an output of a generator neural network. For example, the disclosed systems utilizes a base encoder of the generator neural network to generate a feature set from a semantic label map of a scene. The disclosed system then utilizes a bottom-up encoder to extract multi-resolution features and generate a latent code from the feature set. Furthermore, the disclosed system determines a spatial feature tensor by utilizing a top-down encoder to up-sample and aggregate the multi-resolution features. The disclosed system then utilizes a decoder to generate a synthesized digital image based on the spatial feature tensor and the latent code.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently modifying a generative adversarial neural network using few-shot adaptation to generate digital images corresponding to a target domain while maintaining diversity of a source domain and realism of the target domain. In particular, the disclosed systems utilize a generative adversarial neural network with parameters learned from a large source domain. The disclosed systems preserve relative similarities and differences between digital images in the source domain using a cross-domain distance consistency loss. In addition, the disclosed systems utilize an anchor-based strategy to encourage different levels or measures of realism over digital images generated from latent vectors in different regions of a latent space.

Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.

Abstract: An image generation system enables user input during the process of training a generative model to influence the model's ability to generate new images with desired visual features. A source generative model for a source domain is fine-tuned using training images in a target domain to provide an adapted generative model for the target domain. Interpretable factors are determined for the source generative model and the adapted generative model. A user interface is provided that enables a user to select one or more interpretable factors. The user-selected interpretable factor(s) are used to generate a user-adapted generative model, for instance, by using a loss function based on the user-selected interpretable factor(s). The user-adapted generative model can be used to create new images in the target domain.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize an iterative neural network framework for generating artistic visual content. For instance, in one or more embodiments, the disclosed systems receive style parameters in the form a style image and/or a text prompt. In some cases, the disclosed systems further receive a content image having content to include in the artistic visual content. Accordingly, in one or more embodiments, the disclosed systems utilize a neural network to generate the artistic visual content by iteratively generating an image, comparing the image to the style parameters, and updating parameters for generating the next image based on the comparison. In some instances, the disclosed systems incorporate a superzoom network into the neural network for increasing the resolution of the final image and adding art details that are associated with a physical art medium (e.g., brush strokes).

Abstract: Upon determining calibration parameters for a single-loop controller that minimize a difference between an input to the single-loop controller and a response from the single-loop controller, calibration parameters for a cascade controller are determined based on the determined calibration parameters for the single-loop controller. The cascade controller includes a secondary controller and a primary controller. The calibration parameters for the cascade controller are provided to a second computer in a vehicle.

Abstract: A polymer-based spherical powder preparation device and preparation process are disclosed. The preparation device comprises a mill milling system and an inductively coupled plasma powder spheroidization system. The mill milling system of the preparation device can achieve ultra-fine grinding of the material at room temperature by applying strong extrusion, shear and circumferential stress to the material; and the inductively coupled plasma powder spheroidization system using high temperature plasma as high temperature heat source, the polymer powder can be heated uniformly, and the melting and cooling rate is fast, so the spheroidization can be completed in a short time. The preparation process of polymer based spherical powder was integrated and continuously produced by the preparation device.

Abstract: The present disclosure relates to an in vivo duct repair device and an operating method thereof. The in vivo duct repair device includes a catheter; a nozzle assembly, two axial ends thereof including a supplying end and a spraying end, respectively, the nozzle assembly having a first material flow channel arranged inside the nozzle assembly, a first material spout communicated with the first material flow channel and spraying a first material radially outwards, a second material flow channel arranged inside the nozzle assembly and isolated from the first material flow channel and a second material spout communicated with the second material flow channel and spraying a second material radially outwards, and the nozzle assembly being configured to enter the catheter and extend the spraying end out of an introducing end of the catheter; and a drive device connected to the supplying end.

Abstract: A vectorized caricature avatar generator receives a user image from which face parameters are generated. Segments of the user image including certain facial features (e.g., hair, facial hair, eyeglasses) are also identified. Segment parameter values are also determined, the segment parameter values being those parameter values from a set of caricature avatars that correspond to the segments of the user image. The face parameter values and the segment parameter values are used to generate a caricature avatar of the user in the user image.

Abstract: A residual material withdrawal control method and a bio-printer are provided. The residual material withdrawal control method includes: obtaining a length of a printing material attached to an exterior of a spray head (12) of a printer; and performing multiple withdrawals on the printing material attached to the exterior of the spray head (12) by a material driving mechanism until the length of the printing material attached to the exterior of the spray head (12) is not higher than an allowable value, according to the length of the printing material. The residual material withdrawal control method can effectively eliminate the printing material attached to the spray head (12) of the printer.

Abstract: Techniques for generating style-transferred images are provided. In some embodiments, a content image, a style image, and a user input indicating one or more modifications that operate on style-transferred images are received. In some embodiments, an initial style-transferred image is generated using a machine learning model. In some examples, the initial style-transferred image comprises features associated with the style image applied to content included in the content image. In some embodiments, a modified style-transferred image is generated by modifying the initial style-transferred image based at least in part on the user input indicating the one or more modifications.