Abstract: In implementations of object boundary generation, a computing device implements a boundary system to receive a mask defining a contour of an object depicted in a digital image, the mask having a lower resolution than the digital image. The boundary system maps a curve to the contour of the object and extracts strips of pixels from the digital image which are normal to points of the curve. A sample of the digital image is generated using the extracted strips of pixels which is input to a machine learning model. The machine learning model outputs a representation of a boundary of the object by processing the sample of the digital image.

Abstract: Image modification styles learned from a limited set of modified images are described. A learned style system receives a selection of one or more modified images serving as a basis for a modification style. For each modified image, this system creates a modification memory, which includes a representation of the image content and modification parameters describing modification of this content to produce the modified image. These modification memories are packaged into style data, used to apply the modification style to input images. When applying a style, the system generates an image representation of an input image and determines measures of similarity between the input image's representation and representations of each modification memory in the style data. The system determines parameters for applying the modification style based, in part, on these similarity measures. The system modifies the input image according to the determined parameters to produce a styled image.

Abstract: In implementations of object boundary generation, a computing device implements a boundary system to receive a mask defining a contour of an object depicted in a digital image, the mask having a lower resolution than the digital image. The boundary system maps a curve to the contour of the object and extracts strips of pixels from the digital image which are normal to points of the curve. A sample of the digital image is generated using the extracted strips of pixels which is input to a machine learning model. The machine learning model outputs a representation of a boundary of the object by processing the sample of the digital image.

Abstract: In implementations of object boundary generation, a computing device implements a boundary system to receive a mask defining a contour of an object depicted in a digital image, the mask having a lower resolution than the digital image. The boundary system maps a curve to the contour of the object and extracts strips of pixels from the digital image which are normal to points of the curve. A sample of the digital image is generated using the extracted strips of pixels which is input to a machine learning model. The machine learning model outputs a representation of a boundary of the object by processing the sample of the digital image.

Abstract: In implementations of object boundary generation, a computing device implements a boundary system to receive a mask defining a contour of an object depicted in a digital image, the mask having a lower resolution than the digital image. The boundary system maps a curve to the contour of the object and extracts strips of pixels from the digital image which are normal to points of the curve. A sample of the digital image is generated using the extracted strips of pixels which is input to a machine learning model. The machine learning model outputs a representation of a boundary of the object by processing the sample of the digital image.

Abstract: Image modification styles learned from a limited set of modified images are described. A learned style system receives a selection of one or more modified images serving as a basis for a modification style. For each modified image, this system creates a modification memory, which includes a representation of the image content and modification parameters describing modification of this content to produce the modified image. These modification memories are packaged into style data, used to apply the modification style to input images. When applying a style, the system generates an image representation of an input image and determines measures of similarity between the input image's representation and representations of each modification memory in the style data. The system determines parameters for applying the modification style based, in part, on these similarity measures. The system modifies the input image according to the determined parameters to produce a styled image.

Abstract: Embodiments of the present invention are directed to facilitating images with selective application of the long-exposure effect. In accordance with some embodiments of the present invention, virtual long-exposure image comprising a plurality of aligned frames is provided and a selection of a region of pixels in the virtual long-exposure image is received. The virtual long-exposure image is combined with one of the frames forming the virtual long-exposure image to create a selective virtual long-exposure image. The selective virtual long-exposure image comprises a visible portion of the original virtual long-exposure image and a visible portion of the individual frame that corresponds to the selected region of pixels. Additional frames may be combined with the virtual long-exposure image to create a plurality of selective virtual long-exposure image options, and the user may select one for continued use or for saving.

Abstract: Methods and systems are provided for identifying parameter image adjustments. In embodiments, a set of candidate parameter values associated with a parameter to be analyzed in association with an image is identified. Subsequently, the image is rendered in accordance with each candidate parameter value to generate a set of rendered images. A neural network can then be used to identify a parameter image adjustment to apply to the image based on features associated with the set of rendered images. The neural network can be trained based on a comparison of the identified parameter image adjustment and a reference parameter value associated with the parameter being analyzed.

Abstract: Embodiments of the present invention are directed to facilitating images with selective application of the long-exposure effect. In accordance with some embodiments of the present invention, virtual long-exposure image comprising a plurality of aligned frames is provided and a selection of a region of pixels in the virtual long-exposure image is received. The virtual long-exposure image is combined with one of the frames forming the virtual long-exposure image to create a selective virtual long-exposure image. The selective virtual long-exposure image comprises a visible portion of the original virtual long-exposure image and a visible portion of the individual frame that corresponds to the selected region of pixels. Additional frames may be combined with the virtual long-exposure image to create a plurality of selective virtual long-exposure image options, and the user may select one for continued use or for saving.

Abstract: Methods and systems are provided for identifying parameter image adjustments. In embodiments, a set of candidate parameter values associated with a parameter to be analyzed in association with an image is identified. Subsequently, the image is rendered in accordance with each candidate parameter value to generate a set of rendered images. A neural network can then be used to identify a parameter image adjustment to apply to the image based on features associated with the set of rendered images. The neural network can be trained based on a comparison of the identified parameter image adjustment and a reference parameter value associated with the parameter being analyzed.

Abstract: Techniques and systems are described to render digital images on a defined substrate. In an example, a three-dimensional model is generated of the digital image as disposed on a substrate. Generation of the model includes application of a three-dimensional model of a surface of the substrate to the digital image and addition of material properties of the substrate to the three-dimensional model of the digital image). A viewing direction is detected of the three-dimensional model of the digital image, the detecting based on one or more sensors of the computing device. An effect of light is also ascertained on the three-dimensional model of the digital image having the material properties of the substrate at the detected viewing direction. The three-dimensional model of the digital image is rendered based on the detected viewing direction and the ascertained effect of light for display by the computing device.

Abstract: Techniques and systems are described to render digital images on a defined substrate. In an example, a three-dimensional model is generated of the digital image as disposed on a substrate. Generation of the model includes application of a three-dimensional model of a surface of the substrate to the digital image and addition of material properties of the substrate to the three-dimensional model of the digital image). A viewing direction is detected of the three-dimensional model of the digital image, the detecting based on one or more sensors of the computing device. An effect of light is also ascertained on the three-dimensional model of the digital image having the material properties of the substrate at the detected viewing direction. The three-dimensional model of the digital image is rendered based on the detected viewing direction and the ascertained effect of light for display by the computing device.