Adobe, Inc.

Tushar Rajvanshi

Ajay Bedi

Sourabh Gupta

An image generation system generates images of objects under different lighting conditions. An image of an object and lighting conditions for an output image are received. The lighting conditions may specify, for instance, a location and/or color of one or more light sources. The image of the object is decomposed into a shading component and a reflectance component. A machine learning model takes the reflectance component and specified lighting conditions as input, and generates an output image of the object under the specified lighting conditions. In some configurations, the machine learning model may be trained on images of objects labeled with object classes, and the output image may be generated by also providing an object class of the object in the image as input to the machine learning model.

Generating object images with different lighting conditions

In some embodiments, contextual image variations are generated for an input image. For example, a contextual composite image depicting a variation is generated based on a input image and a synthetic image component. The synthetic image component includes contextual features of a target object from the input image, such as shading, illumination, or depth that are depicted on the target object. The synthetic image component also includes a pattern from an additional image, such as a fabric pattern. In some cases, a mesh is determined for the target object. Illuminance values are determined for each mesh block. An adjusted mesh is determined based on the illuminance values. The synthetic image component is based on a combination of the adjusted mesh and the pattern from the additional image, such as a depiction of the fabric pattern with stretching, folding, or other contextual features from the target image.

Generating contextualized image variants of multiple component images

Methods and systems are provided for performing automated capture of images based on emotion detection. In embodiments, a selection of an emotion class from among a set of emotion classes presented, via a graphical user interface, is received. The emotion class can indicate an emotion exhibited by a subject desired to be captured in an image. A set of images corresponding with a video is analyzed to identify at least one image in which a subject exhibits the emotion associated with the selected emotion class. The set of images can be analyzed using at least one neural network that classifies images in association with emotion exhibited in the images. Thereafter, the image can be presented in association with the selected emotion class.

Automated image capture based on emotion detection

In implementations of image composition instruction based on reference image perspective, a template image of a desired object is captured. Reference images including a similar or same object as the desired object are obtained. Directions from a first location of where a template image was captured to a second location where a selected reference image was captured are obtained and exposed in a user interface to allow a user to move to the second location. At the second location, interactive instructions are generated based on a live video stream of the desired to move a camera capturing the video stream until a composition of the video stream is aligned with a composition of the reference image. The camera is configured with settings based on the reference image to capture a composed image having a same composition as the reference image.

Image composition instruction based on reference image perspective

A digital medium environment includes at least one computing device. Systems and techniques are described herein for reassembling and repairing image sections (e.g., torn pieces of an image) by generating masks for the image sections that do not include content of the image sections, and matching image sections along contours (e.g., edges or sides of the image sections) based on shapes of the masks, features of content extracted from the image sections, or combinations thereof, depending on whether an ambiguity is determined. An ambiguity is determined when not all image sections included in the scans are matched by shape, or are redundantly matched. A composite image is reassembled from the image sections based on matching image sections. Furthermore, a composite image is repaired by adding material to the composite image from an additional image (e.g., an image other than the composite image and the image sections).

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Meerut, India

Tushar Rajvanshi