Gopro, Inc.

Parrot

Thomas Derbanne

Maxim Karpushin

Cesar Douady

César Douady

Nicolas Rahmouni

Antoine Meler

Vincent Riaute

Julien Caron

Antoine Monod

Guillaume Matthieu Guérin

Bruno César Douady

Bruno Cesar Douady-Pleven

Michael Serge Andre Kraak

Martin Lefebure

Adrien Cariou

Luis Mario Domenzain

Claire Jonchery

A constraint model may include a representation of feasible viewing window placement within the field of view of a video for stabilization. When the viewing window extends beyond the field of view of the video, the placement of the viewing window may be modified using a constraint model space partition tree. The constraint model space partition tree may include branches that split the constraint model and enable use of greater number of stabilization parameters to stabilize the video.

Constraint model space partition tree for video stabilization

A video may be captured by an image capture device in motion. A stabilization trajectory for the video may reflect stabilization rotational positions to compensate for at least some of the motion of the image capture device. The stabilization trajectory may have a stabilization trajectory length. The stabilization of the visual content may be assessed based on the stabilization trajectory length.

Systems and methods for assessing stabilization of videos

Visual content is captured by an image capture device during a capture duration. The image capture devices experiences change in position during the capture duration. The trajectory of the image capture device is smoothed based on a look ahead of the trajectory. A punchout of the visual content is determined based on the smoothed trajectory. The punchout of the visual content is used to generate stabilized visual content.

Systems and methods for stabilizing videos

An image capture device may capture visual content during a capture duration. The context of capture of the visual content by the image capture device may be assessed. The context of capture of the visual content by the image capture device may be used to determine values of stabilization parameters for the visual content.

Systems and methods for dynamic stabilization adjustment

Images with an optical field of view are captured by an image capture device. An observed trajectory of the image capture device reflects the positions of the image capture device at different moments may be determined. A capture trajectory of the image capture device reflects virtual positions of the image capture device from which video content may be generated. The capture trajectory is determined based on a subsequent portion of the observed trajectory such that a portion of the capture trajectory corresponding to a portion of the observed trajectory is determined based on a subsequent portion of the observed trajectory. Orientations of punch-outs for the images are determined based on the capture trajectory. Video content is generated based on visual content of the images within the punch-outs.

Positions of an image capture device may be used to determine a position-based time-lapse video frame factor. Apparent motion between pairs of images may be used to determine a visual-based time-lapse video frame rate factor. A time-lapse video frame rate for a time-lapse video may be determined based on the position-based time-lapse video frame factor and the visual-based time-lapse video frame rate factor.

Systems and methods for generating time-lapse videos

Video content may be captured by an image capture device during a capture duration. The video content may include video frames that define visual content viewable as a function of progress through a progress length of the video content. Rotational position information may characterize rotational positions of the image capture device during the capture duration. Time-lapse video frames may be determined from the video frames of the video content based on a spatiotemporal metric. The spatiotemporal metric may characterize spatial smoothness and temporal regularity of the time-lapse video frames. The spatial smoothness may be determined based on the rotational positions of the image capture device corresponding to the time-lapse video frames, and the temporal regularity may be determined based on moments corresponding to the time-lapse video frames. Time-lapse video content may be generated based on the time-lapse video frames.

An image capture device continuously generates time-lapse video frames to be included within a time-lapse video using a dynamic time-lapse video frame rate, with the value of the dynamic time-lapse video frame rate changing based on activation of a trigger to change the dynamic time-lapse video frame rate. The dynamic time-lapse video frame rate alternates between different values.

Image capture device for generating time-lapse videos

A video including visual content may be captured by an image capture device in motion. Stabilization performance information for the visual content may be determined. The stabilization performance information may characterize an extent to which desired stabilization is able to be performed using the visual content. The stabilization for the visual content may be changed based on the stabilization performance information.

Systems and methods for changing stabilization of videos

Positions of an image capture device may be used to estimate a time-lapse video frame rate with which time-lapse video frames are generated. The time-lapse video frame rate may be adjusted based on apparent motion between pairs of generated time-lapse video frames. The adjusted time-lapse video frame rate may be used to generate additional time-lapse video frames.

Scene-based automatic white balance correction may be performed by obtaining an input image. A histogram may be computed from an image thumbnail. The scene-based automatic white balance correction may include classifying a scene. The scene-based automatic white balance correction may include learning a first filter and a second filter. The first filter and the second filter may be learned from one or several different instances of the raw image thumbnail, the augmented image thumbnail, the scene classification, or any combination thereof. The scene-based automatic white balance correction may include applying the filter to the histogram to determine white balance correction coefficients and obtain a processed image.

Scene-based automatic white balance

A video may be captured by an image capture device in motion. A stabilized view of the video may be generated by providing a punchout of the video. The punchout of the video may compensate for rotation of the image capture device during capture of the video. Different field of view punchouts, such as wide field of view punchout and linear field of view punchout, may be used to stabilize the video. Different field of view punchouts may provide different stabilization margins to stabilize the video. The video may be stabilized by switching between different field of view punchouts based on the amount of stabilization margin needed to stabilize the video.

Visual content is captured by an image capture device during a capture duration. The image capture devices experiences motion during the capture duration. The intentionality of the motion of the image capture device is determined based on angular acceleration of the image capture device during the capture duration. A punchout of the visual content is determined based on the intentionality of the motion of the image capture device. The punchout of the visual content is used to generate stabilized visual content.

Growing community of inventors

Paris, France

Thomas Derbanne