For Michael Kors’ Shanghai ‘The Walk’ influencer event, NYC creative agency THAT envisioned a photobooth writ large — a three-camera shoot moving down a 50’ walk against an LED wall placing the subjects in scenes shot on location around the world.
THAT wisely tapped NYC tech wizards AV&C, who knew in order to produce an edited multi-camera video, set to music with titles, every 60 seconds for 5 hours straight, they needed an inhuman editor.
For that they brought on Trash TV, a still-stealth startup developing two dovetailing machine learning softwares - one to tag video, one to edit. That provided a crucial piece of the puzzle - a near-instantaneous edit.
Beginning with Trash’s software as the cornerstone, Hard Work Party provided technical direction on a high-bandwidth humans-and-machines video pipeline, beginning at a user-centric check-in and stage management GUI, proceeding through an almost constantly-rolling live stage, onward to capture, ML edit, human edit pass, QA, transcode, and handoff.
The team created a stateful process management system, one where each users’ progress was managed from a handful of customized interfaces like the check-in iPads, a REST API for the automated stages, and browser-based GUIs for the postproduction team.
On the front, that user-centric flow dovetailed with a stage-focused state machine, handling video playback, video capture, and lighting, and projection on a downstage scrim to cover resets, all driven seamlessly by the stage manager’s tablet.
Making all that possible - the pipes - was a stack of high-availability storage with the capability to serve up 32 concurrent streams of ProRes 422 at 1080 while keeping realtime sync with a hot backup. While invisible, that combination of storage and redundancy was critical to making it possible to deliver 273 30-second videos in 5 hours.
The system was seamless and invisible, the videos were viewed by millions, and the event was nominated for a Webby and covered in WWD, Vogue, and probably a billion Chinese-language publications.
Client: Michael Kors
Experience, Software, Systems - Design & Implementation: AV&C
Technical Direction: Noah Norman / Hard Work Party
Machine Learning Edit Systems: TRASH
Nominated: Webbys 2018
November 15, 2017