{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/462bf552-a18e-4ab0-bce8-b77e62468a58","identifier":"462bf552-a18e-4ab0-bce8-b77e62468a58","url":"https://forgecascade.org/public/capsules/462bf552-a18e-4ab0-bce8-b77e62468a58","name":"EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation","text":"# EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation\n\n**Authors:** Ruozhen He, Meng Wei, Ziyan Yang, Vicente Ordonez\n**arXiv:** https://arxiv.org/abs/2605.15199v1\n**Published:** 2026-05-14T17:59:55Z\n\n## Abstract\nMulti-shot video generation extends single-shot generation to coherent visual narratives, yet maintaining consistent characters, objects, and locations across shots remains a challenge over long sequences. Existing evaluations typically use independently generated prompt sets with limited entity coverage and simple consistency metrics, making standardized comparison difficult. We introduce EntityBench, a benchmark of 140 episodes (2,491 shots) derived from real narrative media, with explicit per-shot entity schedules tracking characters, objects, and locations simultaneously across easy / medium / hard tiers of up to 50 shots, 13 cross-shot characters, 8 cross-shot locations, 22 cross-shot objects, and recurrence gaps spanning up to 48 shots. It is paired with a three-pillar evaluation suite that disentangles intra-shot quality, prompt-following alignment, and cross-shot consistency, with a fidelity gate that admits only accurate entity appearances into cross-shot scoring. As a baseline, we propose EntityMem, a memory-augmented generation system that stores verified per-entity visual references in a persistent memory bank before generation begins. Experiments show that cross-shot entity consistency degrades sharply with recurrence distance in existing methods, and that explicit per-entity memory yields the highest character fidelity (Cohen's d = +2.33) and presence among methods evaluated. Code and data are available at https://github.com/Catherine-R-He/EntityBench/.","keywords":["cs.CV","cs.AI"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"},"dateCreated":"2026-05-16T06:00:05.045000Z","dateModified":"2026-05-16T06:00:05.045000Z"}