DollyWood Night Flight Expedition
Technical artist & pipeline developer
Professional work · Super 78 · Ride & media now live
Super 78 produced immersive media for DollyWood Night Flight Expedition. The ride and our team’s media are now live.
1–8 unattended nightly renders, with VR review media prepared for the team.
Problem and constraints
NightFlight Expedition media needed high-resolution output at non-standard dimensions for attraction projection systems. Editors and clients needed VR-viewable reviews before the media was cut into the show. Multiple external vendors also contributed to production.
My ownership
Built the Unreal rendering pipeline and Python render automation. Owned environment optimization and deployed/administered the Linux/Google Cloud Perforce server for 4–5 external vendors and the internal team. Super 78’s broader production is team work; the making-of video is production context.
Implementation and key decisions
Python automation wraps Movie Render Queue to run nightly jobs. Raw output is converted to VR-viewable review media, renamed to project conventions, and filed in dated server folders. Slack alerts report completion and failure. Environment work includes LODs/HLODs, draw-call and texture/material reduction, lighting and shadow optimization, culling, streaming, and GPU/CPU/memory profiling.
Results and supporting evidence
The ride and Super 78’s media are now live, as confirmed by Stephen on October 5, 2026. Published render throughput is 1–8 unattended renders a night. The workflow prepares headset review media for editorial and client review. The official making-of video identifies the production, but does not demonstrate the queue, error handling, or job logs.
Limitations and lessons
Job triggers, failure detection, retry/recovery logic, render times, and hardware are not described. A sanitized job log and render-to-review diagram would strengthen this case study.