# Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix

DevFeed: [Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix](<https://devfeed.tech/articles/toward-more-controllable-ai-video-editing-an-early-research-exploration-at-netflix-143.md>)

Original publisher: [Read original article](<https://netflixtechblog.com/toward-more-controllable-ai-video-editing-an-early-research-exploration-at-netflix-eb8160ed60a2?source=rss----2615bd06b42e---4>)

Author: Netflix Technology Blog

Published: 2026-06-23T00:31:01Z

Content type: article

Language: en

Sources: [Netflix](<https://devfeed.tech/sources/netflix.md>), [Netflix TechBlog - Medium](<https://devfeed.tech/sources/netflix-techblog-medium.md>)

Topics: [Netflix](<https://devfeed.tech/topics/netflix.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [generative](<https://devfeed.tech/tags/generative.md>), [netflix](<https://devfeed.tech/tags/netflix.md>), [research](<https://devfeed.tech/tags/research.md>), [technology](<https://devfeed.tech/tags/technology.md>), [video](<https://devfeed.tech/tags/video.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

## AI overview

Netflix explores generative AI video editing methods designed to give artists precise control over changes while preserving source footage, creative intent, and physical continuity. The article discusses challenges including unintended edits and unnatural physics in professional video workflows.

## Source excerpt

By Zhuoning Yuan, Ta-Ying Cheng, Benjamin Klein, Bahareh Azarnoush Introduction At Netflix, we build technology to help storytellers bring their creative visions to life and to help members discover the stories they love. 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. 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 principle drives our recent work: exploring the benefits of generative AI in ways that protect and expand creative choice, and keeping artists in precise control of their final vision. Recent advancements in AI video editing have demonstrated impressive capabilities in streamlining complex manual editing workflows, but key challenges remain before they can reliably support professional use: Unintended edits: When editing a specific element in a video clip, many methods regenerate the entire video, which can inadvertently alter identity, performance, and other ele