Computational Long Exposure Mobile Photography
Long exposure photography produces stunning imagery, representing moving
elements in a scene with motion-blur. It is generally employed in two
modalities, producing either a foreground or a background blur effect.
Foreground blur images are traditionally captured on a tripod-mounted camera
and portray blurred moving foreground elements, such as silky water or light
trails, over a perfectly sharp background landscape. Background blur images,
also called panning photography, are captured while the camera is tracking a
moving subject, to produce an image of a sharp subject over a background
blurred by relative motion. Both techniques are notoriously challenging and
require additional equipment and advanced skills. In this paper, we describe a
computational burst photography system that operates in a hand-held smartphone
camera app, and achieves these effects fully automatically, at the tap of the
shutter button. Our approach first detects and segments the salient subject. We
track the scene motion over multiple frames and align the images in order to
preserve desired sharpness and to produce aesthetically pleasing motion
streaks. We capture an under-exposed burst and select the subset of input
frames that will produce blur trails of controlled length, regardless of scene
or camera motion velocity. We predict inter-frame motion and synthesize
motion-blur to fill the temporal gaps between the input frames. Finally, we
composite the blurred image with the sharp regular exposure to protect the
sharpness of faces or areas of the scene that are barely moving, and produce a
final high resolution and high dynamic range (HDR) photograph. Our system
democratizes a capability previously reserved to professionals, and makes this
creative style accessible to most casual photographers.
  More information and supplementary material can be found on our project
webpage: https://motion-mode.github.io/
64cc6807c7f30fbf7baf5b7c
Eric Tabellion
64cc6807c7f30fbf7baf5b7d
Nikhil Karnad
64cc6807c7f30fbf7baf5b7e
Noa Glaser
64cc6807c7f30fbf7baf5b7f
Ben Weiss
64cc6807c7f30fbf7baf5b80
David E. Jacobs
64cc6807c7f30fbf7baf5b81
Yael Pritch
Computational Long Exposure Mobile Photography
Long exposure photography produces stunning imagery, representing moving
elements in a scene with motion-blur. It is generally employed in two
modalities, producing either a foreground or a background blur effect.
Foreground blur images are traditionally captured on a tripod-mounted camera
and portray blurred moving foreground elements, such as silky water or light
trails, over a perfectly sharp background landscape. Background blur images,
also called panning photography, are captured while the camera is tracking a
moving subject, to produce an image of a sharp subject over a background
blurred by relative motion. Both techniques are notoriously challenging and
require additional equipment and advanced skills. In this paper, we describe a
computational burst photography system that operates in a hand-held smartphone
camera app, and achieves these effects fully automatically, at the tap of the
shutter button. Our approach first detects and segments the salient subject. We
track the scene motion over multiple frames and align the images in order to
preserve desired sharpness and to produce aesthetically pleasing motion
streaks. We capture an under-exposed burst and select the subset of input
frames that will produce blur trails of controlled length, regardless of scene
or camera motion velocity. We predict inter-frame motion and synthesize
motion-blur to fill the temporal gaps between the input frames. Finally, we
composite the blurred image with the sharp regular exposure to protect the
sharpness of faces or areas of the scene that are barely moving, and produce a
final high resolution and high dynamic range (HDR) photograph. Our system
democratizes a capability previously reserved to professionals, and makes this
creative style accessible to most casual photographers.
  More information and supplementary material can be found on our project
webpage: https://motion-mode.github.io/
A smartphone app automatically captures and composites foreground or background blur effects using computational burst photography techniques.
computational burst photography salient subject detection scene motion tracking image alignment under-exposed burst inter-frame motion prediction motion-blur synthesis image compositing high dynamic range