Google Inc.

David Salesin

Dominik Philemon Kaeser

Brian Curless

William T Freeman

Janne Matias Kontkanen

Navin Sarma

Jamie Aspinall

Ce Liu

Michael Rubinstein

Forrester H Cole

Richard Tucker

Michael Krainin

Tali Dekel

Huiwen Chang

Abhishek Kar

Kyle Sargent

Varun Jampani

Erika Lu

Brain L Curless

Dominik Kaeser

William T Frreeman

A media application selects, from a collection of images associated with a user account, candidate pairs of images, where each pair includes a first static image and a second static image from the user account. The media application applies a filter to select a particular pair of images from the candidate pairs of images. The media application generates, using an image interpolator, one or more intermediate images based on the particular pair of images. The media application generates a video that includes three or more frames arranged in a sequence, where a first frame of the sequence is the first static image, a last frame of the sequence is the second static image, and each of the one or more intermediate images is a corresponding intermediate frame of the sequence between the first frame and the last frame.

Using interpolation to generate a video from static images

A method includes determining, based on an image having an initial viewpoint, a depth image, and determining a foreground visibility map including visibility values that are inversely proportional to a depth gradient of the depth image. The method also includes determining, based on the depth image, a background disocclusion mask indicating a likelihood that pixel of the image will be disoccluded by a viewpoint adjustment. The method additionally includes generating, based on the image, the depth image, and the background disocclusion mask, an inpainted image and an inpainted depth image. The method further includes generating, based on the depth image and the inpainted depth image, respectively, a first three-dimensional (3D) representation of the image and a second 3D representation of the inpainted image, and generating a modified image having an adjusted viewpoint by combining the first and second 3D representation based on the foreground visibility map.

Single image 3D photography with soft-layering and depth-aware inpainting

A computer-implemented method for decomposing videos into multiple layers () that can be re-combined with modified relative timings includes obtaining video data including a plurality of image frames () depicting one or more objects. For each of the plurality of frames, the computer-implemented method includes generating one or more object maps descriptive of a respective location of at least one object of the one or more objects within the image frame. For each of the plurality of frames, the computer-implemented method includes inputting the image frame and the one or more object maps into a machine-learned layer Tenderer model. () For each of the plurality of frames, the computer-implemented method includes receiving, as output from the machine-learned layer Tenderer model, a background layer illustrative of a background of the video data and one or more object layers respectively associated with one of the one or more object maps. The object layers include image data illustrative of the at least one object and one or more trace effects at least partially attributable to the at least one object such that the one or more object layers and the background layer can be re-combined with modified relative timings.

Re-timing objects in video via layered neural rendering

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Sausalito, CA, United States of America

David Salesin