迈向更可控的AI视频编辑：Netflix的早期研究探索

By Zhuoning Yuan, Ta-Ying Cheng, Benjamin Klein, Bahareh Azarnoush

作者：Zhuoning Yuan，Ta-Ying Cheng，Benjamin Klein，Bahareh Azarnoush

Introduction

简介

At Netflix, we build technology to help storytellers bring their creative visions to life and to help members discover the stories they love.

在 Netflix，我们构建技术以帮助故事讲述者将他们的创意愿景变为现实，并帮助会员发现他们喜爱的故事。

To connect stories with diverse audiences around the world, we produce promotional assets, including trailers, teasers, and social short‑form videos, that build on and elevate the original footage. Through close collaboration with the teams crafting these assets, we identified a recurring gap in current tools. Transforming raw footage into a polished final asset often requires complex edits like seamlessly adding new visual elements, patching or replacing backgrounds, or removing unwanted objects without breaking the scene’s physical continuity. These tasks typically demand hours of specialized manual editing work. While recent generative video editing models show promise, they often struggle to preserve the integrity of the source footage. Many methods regenerate every pixel to make an edit, which can fail to isolate changes and inadvertently alter elements that should remain untouched. To execute these tasks effectively, artists need tools that empower them to dictate exactly what changes and how it changes.

为了将故事与全球 diverse 受众联系起来，我们制作了宣传素材，包括预告片、先导片和社交短视频，这些素材在原始素材的基础上进行构建和提升。通过与制作这些素材的团队密切合作，我们发现当前工具中存在一个反复出现的差距。将原始素材转化为精美的最终素材通常需要复杂的编辑，例如无缝添加新的视觉元素、修补或替换背景，或者在不破坏场景物理连续性的情况下移除不需要的物体。这些任务通常需要数小时的专业手动编辑工作。虽然最近的生成式视频编辑模型展现出了潜力，但它们通常难以保持原始素材的完整性。许多方法通过重新生成每个像素来进行编辑，这可能无法隔离更改，并无意中改变了应保持不变的元素。为了有效地执行这些任务，艺术家需要能够赋予他们权力来准确指定更改内容及其更改方式的工具。

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Our research goal is to make this process easier for artists. We’re deliberate about where and how AI is applied, ensuring that the technology always serves the creative intent. That p...