Pre-Publication V2. The application of machine learning models intended to automate the frame-by-frame isolation of subjects, objects, or regions within video footage, a process traditionally performed manually by artists tracing subject boundaries on each frame. Automated rotoscoping uses techniques including but not limited to such as instance segmentation, optical flow, and temporal propagation to generate mattes or masks that track subjects across time, accounting for motion, occlusion, lighting changes, and edge complexity. Output typically requires varying degrees of human review and correction depending on scene complexity. Applications include compositing, visual effects, color grading isolation, and background replacement.
Deliberation Summary:
(1) Definition describes a task outcome rather than a technology, as the work has long been automated without AI. (2) Utilize intent-based phrasing, rather than outcomes only.
Pre-Publication V1. The application of machine learning models to automate the frame-by-frame isolation of subjects, objects, or regions within video footage, a process traditionally performed manually by artists tracing subject boundaries on each frame. Automated rotoscoping uses techniques such as instance segmentation, optical flow, and temporal propagation to generate mattes or masks that track subjects across time, accounting for motion, occlusion, lighting changes, and edge complexity. Output typically requires varying degrees of human review and correction depending on scene complexity. Applications include compositing, visual effects, color grading isolation, and background replacement.