SBIR-STTR Award

Filmmaking for Everyone: Computational Video Editing
Award last edited on: 7/22/2020

Sponsored Program
SBIR
Awarding Agency
NSF
Total Award Amount
$561,582
Award Phase
2
Solicitation Topic Code
IT
Principal Investigator
Genevieve Patterson

Company Information

Trash Inc

98 4th Street Suite 401
Brooklyn, NY 11231
   (520) 275-3170
   N/A
   www.trash.app
Location: Single
Congr. District: 07
County: Kings

Phase I

Contract Number: 1842850
Start Date: 1/1/2019    Completed: 10/31/2019
Phase I year
2019
Phase I Amount
$224,734
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from addressing video editing as a software problem. All the hurdles that surround this space - the clunkiness of having to poke at a timeline of clips with your fingers on a rectangle of glass, the time and technical skill required to even know how to put those clips together in a pleasing sequence, and the cost to have someone else do it for you - are all problems that can be solved with computational video editing. The cameras in mobile phones are the most important contemporary tool for artistic expression and cultural communication. The company's mobile video editing platform gives young and economically disadvantaged creators (who may only have a mobile device camera) access to the narrative format of video. With the growing adoption of mobile video by creators and viewers in every corner of the globe, high-quality video editing tools are increasingly needed for mobile platforms. This Small Business Innovation Research (SBIR) Phase I project will investigate the use of video understanding techniques that support the creation of artistic and cultural output. This project will develop algorithms, representations, and datasets that allow consumer-grade devices such as smartphones, tablets, and commodity PCs to understand video and generate narrative video sequences. The goal of this Phase I project is at the intersection of human-computer interaction, computer vision, and computational videography. This project will explore rich semantic embedding spaces, end-to-end trained multi-task neural networks, and large-scale data and their application to video manipulation, enhancement, and the ultimate goal of automated film editing.

Phase II

Contract Number: 1950115
Start Date: 4/1/2020    Completed: 3/31/2022
Phase II year
2020
Phase II Amount
$336,848
The broader impacts of this Small Business Innovation Research (SBIR) Phase II project enable improved user-generated video content. Video online platforms have radically changed how people communicate, learn, and inspire. Unfortunately, many potentially inspiring videos are lost or never shared due to an inability to edit them into compelling vignettes. Most tools for editing video are expensive, difficult to learn, and time-consuming. This project’s research enables consumers or nascent businesses to make polished, professional videos with a single phone click through the use of computational cinematography and techniques from deep learning and artificial intelligence (AI). The delivered software solution will produce high-quality edited footage within minutes, compared with a human editor requiring hours. This project combines the analysis of video using computer vision with editing algorithms to empower new creators to participate in this fast-growing medium. This Small Business Innovation Research (SBIR) Phase II project will produce AI-powered software for automatically editing raw video footage into quality short films on a mobile phone platform. The proposed project will integrate advanced computational video manipulation, computer vision, and audio recognition. The prototype AI editor will select relevant content from source footage and synchronize it to music, using only the restricted computational resources of a typical mobile platform. The AI makes decisions based on the video content, the music content, and narrative editing styles learned from a large dataset of similar films. This project will deliver AI-based editing technology that trims and arranges input footage based on the spoken dialogue in the input videos. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.