EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation

• URL: http://arxiv.org/abs/2605.15199v1
• Date: Thu, 14 May 2026 17:59:55 GMT
• Title: EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation
• Abstract summary: We introduce EntityBench, a benchmark of 140 episodes (2,491 shots) derived from real narrative media.<n>It is paired with a three-pillar evaluation suite that disentangles intra-shot quality, prompt-following alignment, and cross-shot consistency.<n> Experiments show that cross-shot entity consistency degrades sharply with recurrence distance in existing methods.
• Score: 16.322441569982647
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multi-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/.

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