Patents by Inventor Markus Woodson
Markus Woodson has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11949964Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.Type: GrantFiled: September 9, 2021Date of Patent: April 2, 2024Assignee: Adobe Inc.Inventors: Bryan Russell, Ruppesh Nalwaya, Markus Woodson, Joon-Young Lee, Hailin Jin
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Publication number: 20230276084Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that generate a temporally remapped video that satisfies a desired target duration while preserving natural video dynamics. In certain instances, the disclosed systems utilize a playback speed prediction machine-learning model that recognizes and localizes temporally varying changes in video playback speed to re-time a digital video with varying frame-change speeds. For instance, to re-time the digital video, the disclosed systems utilize the playback speed prediction machine-learning model to infer the slowness of individual video frames. Subsequently, in certain embodiments, the disclosed systems determine, from frames of a digital video, a temporal frame sub-sampling that is consistent with the slowness predictions and fit within a target video duration.Type: ApplicationFiled: March 16, 2023Publication date: August 31, 2023Inventors: Simon Jenni, Markus Woodson, Fabian David Caba Heilbron
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Patent number: 11610606Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that generate a temporally remapped video that satisfies a desired target duration while preserving natural video dynamics. In certain instances, the disclosed systems utilize a playback speed prediction machine-learning model that recognizes and localizes temporally varying changes in video playback speed to re-time a digital video with varying frame-change speeds. For instance, to re-time the digital video, the disclosed systems utilize the playback speed prediction machine-learning model to infer the slowness of individual video frames. Subsequently, in certain embodiments, the disclosed systems determine, from frames of a digital video, a temporal frame sub-sampling that is consistent with the slowness predictions and fit within a target video duration.Type: GrantFiled: February 25, 2022Date of Patent: March 21, 2023Assignee: Adobe Inc.Inventors: Simon Jenni, Markus Woodson, Fabian David Caba Heilbron
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Patent number: 11244204Abstract: In implementations of determining video cuts in video clips, a video cut detection system can receive a video clip that includes a sequence of digital video frames that depict one or more scenes. The video cut detection system can determine scene characteristics for the digital video frames. The video cut detection system can determine, from the scene characteristics, a probability of a video cut between two adjacent digital video frames having a boundary between the two adjacent digital video frames that is centered in the sequence of digital video frames. The video cut detection system can then compare the probability of the video cut to a cut threshold to determine whether the video cut exists between the two adjacent digital video frames.Type: GrantFiled: May 20, 2020Date of Patent: February 8, 2022Assignee: Adobe Inc.Inventors: Oliver Wang, Nico Alexander Becherer, Markus Woodson, Federico Perazzi, Nikhil Kalra
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Publication number: 20210409836Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.Type: ApplicationFiled: September 9, 2021Publication date: December 30, 2021Inventors: Bryan Russell, Ruppesh Nalwaya, Markus Woodson, Joon-Young Lee, Hailin Jin
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Publication number: 20210365742Abstract: In implementations of determining video cuts in video clips, a video cut detection system can receive a video clip that includes a sequence of digital video frames that depict one or more scenes. The video cut detection system can determine scene characteristics for the digital video frames. The video cut detection system can determine, from the scene characteristics, a probability of a video cut between two adjacent digital video frames having a boundary between the two adjacent digital video frames that is centered in the sequence of digital video frames. The video cut detection system can then compare the probability of the video cut to a cut threshold to determine whether the video cut exists between the two adjacent digital video frames.Type: ApplicationFiled: May 20, 2020Publication date: November 25, 2021Applicant: Adobe Inc.Inventors: Oliver Wang, Nico Alexander Becherer, Markus Woodson, Federico Perazzi, Nikhil Kalra
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Patent number: 11146862Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.Type: GrantFiled: April 16, 2019Date of Patent: October 12, 2021Assignee: ADOBE INC.Inventors: Bryan Russell, Ruppesh Nalwaya, Markus Woodson, Joon-Young Lee, Hailin Jin
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Publication number: 20200336802Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.Type: ApplicationFiled: April 16, 2019Publication date: October 22, 2020Inventors: Bryan Russell, Ruppesh Nalwaya, Markus Woodson, Joon-Young Lee, Hailin Jin