ShutterApp: Spatio‐temporal Exposure Control for Videos

Salamon, Nestor Z.; Billeter, Markus; Eisemann, Elmar

doi:10.1111/cgf.13870

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…There is a large body of prior work on synthesizing motion-blur, in the context of non-photorealistic rendering [Lee et al 2009], stop-motion animation [Brostow and Essa 2001], or 3D computer graphics rendering in real-time [Rønnow et al 2021] or offline [Lehtinen et al 2011;Navarro et al 2011]. There is work describing single photograph post-processing interactive applications to create artistic motion effects [Luo et al 2018[Luo et al , 2020Teramoto et al 2010] or that process multiple previously stabilized images and can achieve non-physical renditions [Salamon et al 2019].…”

Section: Synthesizing Motion Trailsmentioning

confidence: 99%

Computational Long Exposure Mobile Photography

et al. 2023

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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.

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