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Google Research proposes a multi-agent framework that automatically generates long-video narratives
Google Research has released a unified multi-agent framework for automatically generating temporally coherent long-video narratives, addressing identity drift and cascading failures in existing linear pipelines. Built on top of Gemini and Veo as an orchestration layer, the framework consists of four parts: AI video co-director uses multi-armed bandits for global creative search, CANVAS plans multi-shot visual continuity through persistent visual memory, A²RD generates segment by segment autoregressively while maintaining multimodal video memory, and VQQA uses visual question answering to generate semantic gradients for closed-loop prompt optimization. Evaluations show that AI video co-director reaches a peak quality score of 81.4 on GenAD-Bench, CANVAS improves scene-reappearance continuity on ST-Bench and HardContinuityBench, A²RD reduces layout drift on VBench-Long and LVBench-C, and VQQA achieves quality gains on T2V-CompBench, VBench2, and VBench-I2V.
Google Research has released a unified multi-agent framework for automatically generating temporally coherent long-video narratives, addressing identity drift and cascading failures in existing linear pipelines. Built on top of Gemini and Veo as an orchestration layer, the framework consists of four parts: AI video co-director uses multi-armed bandits for global creative search, CANVAS plans multi-shot visual continuity through persistent visual memory, A²RD generates segment by segment autoregr
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