自动化连贯长视频生成

Yale Song and Yiwen Song, Research Scientists, Google We introduce a unified multi-agent framework that autonomously generates temporally consistent, long-form video narratives, overcoming the identity drift and cascading failures of current linear AI pipelines. Recent advancements in video diffusion demonstrate remarkable high-fidelity generation with models that can render realistic scenes in seconds. However, while diffusion models generate high-fidelity video clips, transforming them into coherent long storytelling engines remains challenging. Most existing agentic pipelines automate this process via chained modules but suffer from semantic drift (subtle shifts in character attire or scenery across shots) and cascading failures (e.g., an upstream asset artifact corrupting downstream video synthesis) due to independent, handcrafted prompting. Because early errors propagate and break long-horizon consistency, the process often requires exhaustive manual intervention. From a structural perspective, this reflects the classical credit assignment problem, as terminal failures are difficult to trace back to specific prompts. Furthermore, existing methods suffer from feature drift , where entities and environments gradually change unintentionally, or content collapse , where narratives fail to progress meaningfully. Today, we introduce our research on an AI video co-director, a unified, multi-agent framework that explicitly plans visual continuity in multi-shot narratives. Built as an orchestration layer on top of Gemini and Veo , this framework natively inherits safety mechanisms like SynthID watermarking . By treating long-form generation as a global optimization and world-state tracking problem, we have developed a suite of frameworks — Co-Director (to appear at COLM 2026 ), CANVAS (to appear at EMNLP 2026 ), A²RD , and VQQA —that translate high-level human creative specification into execution by automating repetitive orchestration tasks, from multi-model prompting and shot chaining to closed-loop visual refinement. We designed these frameworks to act as responsive creative partners that abstract away the burdens of maintaining visual continuity, freeing users to concentrate on the art of storytelling. This architecture decouples creative synthesis from consistency by modeling quality as a test-time objective. Across comprehensive evaluations, our framework demonstrates substantial gains in multi-shot narrative consistency and character persistence, successfully generating minutes-long videos while mitigating visual drift and pipeline error propagation. To solve the multi-faceted problem of long-horizon video, we broke the research down into four foundational pillars, each addressing a specific bottleneck in the generative pipeline. To ensure semantic coherence across an entire video, we present AI video co-director , a hierarchical multi-agent framework formalizing video storytelling as a global optimization problem. Rather than relying on rigid, linear prompt chains, we introduce hierarchical parameterization: a multi-armed bandit (MAB) globally identifies promising creative directions. This formalizes the creative process as a search for the optimal balance between exploration of novel narrative strategies with the exploitation of effective creative configurations. The system samples abstract creative trajectories — such as combining an informational strategy with a vignette narrative mode and a specific aesthetic archetype — and dynamically injects these into the system prompts of sub-agents. This top-down steering guarantees that the entire pipeline operates under a unified vision. Because our AI video co-director framework operates as an orchestration layer, it achieves this vision by feeding these structured prompts directly into the foundational Gemini and Veo models (though its model-agnostic architecture allows it to sit on top of any foundation generative model). This architecture ensures that all generated