DUOSTATE: EVALUATING SELECTIVE STATE CONTROL IN VIDEO GENERATION

Following a video script requires more than keeping characters recognisable: the intended person must change, the other must remain unchanged, and a later instruction may require restoring an earlier state. We introduce CharacterLedgerBench, a framework for evaluating this selective state control. A paired protocol tests four branches of the same two-character scene: no edit, an edit to either character, and an edit followed by restoration after occlusion. A region-grounded evidence-to-ledger judge tracks the characters, records visible attributes from timestamped regions, and checks their state changes against the script. It distinguishes recognition of the requested states from completion of the full process in the prescribed order and time windows. On an independent diagnostic set, regional evidence and ledger-based rules improve success-detection F1 from 0.653 to 0.869 over direct full-frame judging. Strict verification accepts only 5.0% of the benchmark's original reversal clips. Providing enough time for every stage and matching edits across roles improves some results, but state control remains difficult. CharacterLedgerBench connects these failures to specific characters and events, establishing an evaluation target beyond visual consistency.