A Practical Framework for Evaluating Reference-Based AI Video Recasting

Demos of AI video generation are hard to compare because a single appealing frame says little about motion retention or subject consistency. The article proposes a fixed input set and short review rubric for testing reference-based recasting, where an existing clip's timing and composition guide a new output. It recommends a short source clip with one subject and one clear action, plus reference images showing the angles the motion requires. Evaluation rests on four questions covering temporal fidelity, identity consistency, physical contact, and publishing fitness. The author advises changing only one input per run so results remain explainable and repeatable.