Google Inc.

Nathan Frey

Brian James Mulford

Zheng Sun

Yuan Zhang

Alexandros Panagopoulos

Yinquan Hao

Weilong Yang

Vinay Kotikalapudi Sriram

Sandor Miklos Szego

Yifan Zou

The present disclosure provides systems, methods, and computer program products for performing automated non-linear editing style transfer. A computer-implemented method may include determining one or more shot boundaries in a video, analyzing identified content in each of one or more shots in the video based on performing object detection, determining an editing style for each of the one or more shots in the video based at least in part on measuring motion across frames within the respective shots, determining a content segment to adjust from a set of target content based on analyzing the set of target content in view of the identified content and the determined editing style of a shot from the video, and automatically adjusting the content segment from the set of target content based at least in part on modifying the content segment with the determined editing style of the shot from the video.

Automatic non-linear editing style transfer

A method for replacing an object in an image. The method may include identifying a first object at a position within a first image, masking, based on the first image and the position of the first object, a target area to produce a masked image, generating, based on the masked image and an inpainting machine learning model, a second image different from the first image, the inpainting machine learning model being trained using a difference between the target area of training images and content of generated images at location corresponding to the target area of the training images, generating, based on the masked image and the second image, a third image, and adding, to the third image, a new object different from the first object.

Image replacement inpainting

A method for efficient dynamic video rendering is described for certain implementations. The method may include identifying a file for rendering a video comprising one or more static layers and one or more dynamic layers, detecting, based on analyzing one or more fields of the file for rendering a video, the one or more static layers and the one or more dynamic layers, wherein each dynamic layer comprises a comment that indicates a variable component, rendering the one or more static layers of the file, receiving, from a user device, a request for the video that includes user information, determining, based on the user information, variable definitions designated to be inserted into a dynamic layer, rendering the one or more dynamic layers using the variable definitions, and generating a composite video for playback from the rendered one or more static layers and the rendered one or more dynamic layers.

Efficiently rendering video having dynamic components

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating videos. In one aspect, a method comprises: receiving: (i) an input video comprising a sequence of video frames, and (ii) data indicating a target object type; processing the input video to generate tracking data that identifies and tracks visual locations of one or more instances of target objects of the target object type in the input video; generating a plurality of sub-videos based on the input video and the tracking data, including: for each sub-video, generating a respective sequence of sub-video frames that are each extracted from a respective video frame of the input video to include a respective instance of a given target object from among the identified target objects of the target object type; and generating an output video that comprises the plurality of sub-videos.

Generating videos

Described herein are systems and methods of converting media dimensions. A device may identify a set of frames from a video in a first orientation as belonging to a scene. The device may receive a selected coordinate on a frame of the set of frames for the scene. The device may identify a first region within the frame including a first feature corresponding to the selected coordinate and a second region within the frame including a second feature. The device may generate a first score for the first feature and a second score for the second feature. The first score may be greater than the second score based on the first feature corresponding to the selected coordinate. The device may crop the frame to include the first region and the second region within a predetermined display area comprising a subset of regions of the frame in a second orientation.

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Mountain View, CA, United States of America

Nathan Frey