Abstract: Disclosed are systems, methods, and computer-readable storage media to enhance an image on a mobile computing device. In some aspects, a user interface on the mobile computing device provides a plurality of image filters, each of the image filters associated with a different replacement sky type. Upon application of one of the plurality of image filters to an image, the mobile computing device is configured to identify a sky portion of the image, modify a color mapping of a non-sky portion of the image, and replace the sky portion of the image with replacement sky data corresponding to the applied image filter.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: Disclosed are systems, methods, and computer-readable storage media to modify image content. One aspect includes identifying, by one or more electronic hardware processors, an image and content within the image, determining, by the one or more electronic hardware processors, a sky region of the image, determining, by the one or more electronic hardware processors, whether the content within the image is located within the sky region of the image, and in response to the content being within the sky region of the image, modifying, by the one or more electronic hardware processors, the content based on fractal Brownian motion.

Abstract: A neural network training method, an image processing method, and apparatuses thereof are provided. The neural network training method includes obtaining a first domain image and a second domain image, where the first domain image and the second domain image are unpaired images in different domains; obtaining a scaled first domain image by scaling, at an iteration, the first domain image; obtaining a training patch by cropping the scaled first domain image, where each training patch has a same number of pixels with different contents; inputting the training patch into the neural network at the iteration, and outputting an output patch; calculating a contrastive loss based on a query sub-patch and negative sub-patches selected from the training patch and a corresponding positive sub-patch selected from the output patch; and updating model parameters of the neural network based on the contrastive loss and a generative adversarial network loss.

Abstract: A method for processing an image, an electronic device and a storage medium are provided. The method includes: obtaining an original image including a target object; obtaining an auxiliary line by extracting semantic information from the original image, the auxiliary line including at least one of: an area boundary line of the target object and a part contour line of the target object; obtaining a prediction result for a semantic line by inputting an image stitched from the auxiliary line and the original image to a predictive neural network, the auxiliary line guiding the predictive neural network to obtain the prediction result, the prediction result indicating a probability that a pixel in the original image is a pixel in the semantic line, the semantic line being used for rendering the target object; and obtaining the semantic line based on the prediction result for the semantic line.

Abstract: A hair rendering method includes acquiring a target video containing hair information, and selecting a target image frame from image frames of the target video, acquiring a texture image of the target image frame, wherein the texture image is an image in a texture format which records motion states and state change information of one or more pixel points in the target image frame. The method further includes acquiring a first target motion state and target state change information of a first target particle region from the texture image; determining a second target motion state of the first target particle region in a next image frame according to the first target motion state and the target state change information, and rendering the hair region by updating a motion state in the texture image according to the second target motion state.

Abstract: A dolly zoom effect can be applied to one or more images captured via a resource-constrained device (e.g., a mobile smartphone) by manipulating the size of a target feature while the background in the one or more images changes due to physical movement of the resource-constrained device. The target feature can be detected using facial recognition or shape detection techniques. The target feature can be resized before the size is manipulated as the background changes (e.g., changes perspective).

Abstract: Various embodiments provide for systems, methods, and computer-readable storage media for annotating a digital image with a texture fill. An annotation system may receive a user input defining a border separating a first portion of a target digital image from a second portion of the target digital image. The annotation system may then generate a contour mask, such as a binary mask, for the target digital image based on the user-defined border. The annotation system may then apply a media overlay to the target image based on the contour mask. In particular, the contour mask can define portions of the target digital image such that the annotation system will apply a media overlay to at least one of those portions while not applying the media overlay to remaining portions of the target digital image.

Abstract: A dolly zoom effect can be applied to one or more images captured via a resource-constrained device (e.g., a mobile smartphone) by manipulating the size of a target feature while the background in the one or more images changes due to physical movement of the resource-constrained device. The target feature can be detected using facial recognition or shape detection techniques. The target feature can be resized before the size is manipulated as the background changes (e.g., changes perspective).

Abstract: A method for processing images includes recognizing a hair region in a target image; acquiring a hairline direction of each pixel point in the hair region; acquiring an intermediate image by smoothing the hair region based on the hairline direction; and acquiring a virtual hairline along the hairline direction, and acquiring a first image by superimposing the virtual hairline to the hair region in the intermediate image.

Abstract: Disclosed are systems, methods, and computer-readable storage media to enhance an image on a mobile computing device. In some aspects, a user interface on the mobile computing device provides a plurality of image filters, each of the image filters associated with a different replacement sky type. Upon application of one of the plurality of image filters to an image, the mobile computing device is configured to identify a sky portion of the image, modify a color mapping of a non-sky portion of the image, and replace the sky portion of the image with replacement sky data corresponding to the applied image filter.

Abstract: The present disclosure provides a method, device and non-transitory computer storage medium for processing an image. The method includes: obtaining an image by a monocular camera; extracting image features with different levels based on the image; determining a fused feature by fusing the image features with different levels; and determining a depth distribution feature map of the image based on the fused feature, where a pixel value of each pixel point in the depth distribution feature map is a depth value.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: Disclosed are systems, methods, and computer-readable storage media to enhance an image on a mobile computing device. In some aspects, a user interface on the mobile computing device provides a plurality of image filters, each of the image filters associated with a different replacement sky type. Upon application of one of the plurality of image filters to an image, the mobile computing device is configured to identify a sky portion of the image, modify a color mapping of a non-sky portion of the image, and replace the sky portion of the image with replacement sky data corresponding to the applied image filter.

Abstract: An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Abstract: Disclosed are systems, methods, and computer-readable storage media to modify image content. One aspect includes identifying, by one or more electronic hardware processors, an image and content within the image, determining, by the one or more electronic hardware processors, a sky region of the image, determining, by the one or more electronic hardware processors, whether the content within the image is located within the sky region of the image, and in response to the content being within the sky region of the image, modifying, by the one or more electronic hardware processors, the content based on fractal Brownian motion.

Abstract: A method for processing images includes recognizing a hair region in a target image; acquiring a hairline direction of each pixel point in the hair region; acquiring an intermediate image by smoothing the hair region based on the hairline direction; and acquiring a virtual hairline along the hairline direction, and acquiring a first image by superimposing the virtual hairline to the hair region in the intermediate image.

Abstract: The present specification describes systems and methods for automatically generating personalized blendshapes from actor performance measurements, while preserving the semantics of a template facial animation rig. The disclosed inventions facilitate the creation of an ensemble of realistic digital double face rigs for each individual with consistent behaviour across the set with sophisticated iterative optimization techniques.

Abstract: Disclosed are systems, methods, and computer-readable storage media to modify image content. One aspect includes identifying, by one or more electronic hardware processors, an image and content within the image, determining, by the one or more electronic hardware processors, a sky region of the image, determining, by the one or more electronic hardware processors, whether the content within the image is located within the sky region of the image, and in response to the content being within the sky region of the image, modifying, by the one or more electronic hardware processors, the content based on fractal Brownian motion.

Abstract: Systems and methods are provided for receiving an image from a camera of a mobile device, analyzing the image to determine a subject of the image, segmenting the subject of the image to generate a mask indicating an area of the image comprising the subject of the image, applying a bokeh effect to a background region of the image to generate a processed background region, generating an output image comprising the subject of the image and the processed background region, and causing the generated output image to display on a display of the mobile device.