视频生成领域终于有了严肃的实体一致性基准，做多镜头叙事生成的研究者可以直接用这套评估体系，EntityMem 的记忆方案也值得复现试试。
多镜头视频生成面临跨镜头实体（角色、物体、场景）一致性难题，现有评估方法覆盖有限且指标简单。研究者推出 EntityBench 基准，包含 140 个剧集（2491 个镜头），按难易分三档，最长 50 个镜头、13 个跨镜头角色、8 个场景、22 个物体，并设计三支柱评估套件（画质、指令遵循、跨镜头一致性），仅通过保真门控的实体才计入一致性评分。作为基线，提出 EntityMem 记忆增强系统，在生成前将已验证的实体视觉参考存入持久记忆库。实验表明现有方法跨镜头一致性随镜头间隔急剧下降，而 EntityMem 在角色保真度（Cohen's d = +2.33）和出现率上最优。代码和数据已开源。
EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation
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/.