Google Inc.

Seyed Mohammad Mehdi Sajjadi

Jonathan Tilton Barron

Alexey Dosovitskiy

Daniel Christopher Duckworth

Ricardo Martin-Brualla

Ricardo Martin Brualla

Matthew Alun Brown

Raviteja Vemulapalli

Noha Waheed Ahmed Radwan

Noha Radwan

Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, 'in the wild' photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).

View synthesis robust to unconstrained image data

The present disclosure provides systems and methods to increase resolution of imagery. In one example embodiment, a computer-implemented method includes obtaining a current low-resolution image frame. The method includes obtaining a previous estimated high-resolution image frame, the previous estimated high-resolution frame being a high-resolution estimate of a previous low-resolution image frame. The method includes warping the previous estimated high-resolution image frame based on the current low-resolution image frame. The method includes inputting the warped previous estimated high-resolution image frame and the current low-resolution image frame into a machine-learned frame estimation model. The method includes receiving a current estimated high-resolution image frame as an output of the machine-learned frame estimation model, the current estimated high-resolution image frame being a high-resolution estimate of the current low-resolution image frame.

Frame-recurrent video super-resolution

Growing community of inventors

Berlin, Germany

Seyed Mohammad Mehdi Sajjadi