Systems and methods for generating defocus blur effects
AssigneeFacebook Technologies, LLC
InventorsDouglas Robert Lanman, Matthew Steven Chapman, Alexander Jobe Fix, Anton S. Kaplanyan, Lei Xiao
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AssigneeFacebook Technologies, LLC
InventorsDouglas Robert Lanman, Matthew Steven Chapman, Alexander Jobe Fix, Anton S. Kaplanyan, Lei Xiao
Estimating the term…
In one embodiment, a computing system may receive current eye-tracking data associated with a user of a head-mounted display. The system may dynamically adjust a focal length of the head-mounted display based on the current eye-tracking data. The system may generate an in-focus image of a scene and a corresponding depth map of the scene. The system may generate a circle-of-confusion map for the scene based on the depth map. The circle-of-confusion map encodes a desired focal surface in the scene. The system may generate, using a machine-learning model, an output image with a synthesized defocus-blur effect by processing the in-focus image, the corresponding depth map, and the circle-of-confusion map of the scene. The system may display the output image with the synthesized defocus-blur effect to the user via the head-mounted display having the adjusted focal length.
A method for using a machine-learning model to generate images with defocus-blur effects based on in-focus input images and corresponding depth maps, the method comprising, by a computing system:
One or more computer-readable non-transitory storage media embodying software for using a machine-learning model to generate images with defocus-blur effects based on in-focus input images and corresponding depth maps, wherein the software is operable when executed to:
A system for using a machine-learning model to generate images with defocus-blur effects based on in-focus input images and corresponding depth maps, the system comprising:
Dependencies are inferred from the claim text.