# A Text-Native Interface for Generative Video Authoring

arXiv: https://arxiv.org/abs/2603.09072 · HTML: https://mlsanity.com/paper/2603.09072/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [Text-based Editing of Talking-head Video](https://mlsanity.com/paper/1906.01524/) (2019)
- [LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing](https://mlsanity.com/paper/2402.10294/) (2024)
- [AVscript: Accessible Video Editing with Audio-Visual Scripts](https://mlsanity.com/paper/2302.14117/) (2023)
- [Compositional Structures as Substrates for Human-AI Co-creation Environment: A Design Approach and A Case Study](https://mlsanity.com/paper/2503.04103/) (2025)

## Abstract (arXiv)

Everyone can write their stories in freeform text format -- it's something we all learn in school. Yet storytelling via video requires one to learn specialized and complicated tools. In this paper, we introduce Doki, a text-native interface for generative video authoring, aligning video creation with the natural process of text writing. In Doki, writing text is the primary interaction: within a single document, users define assets, structure scenes, create shots, refine edits, and add audio. We articulate the design principles of this text-first approach and demonstrate Doki's capabilities through a series of examples. To evaluate its real-world use, we conducted a week-long deployment study with participants of varying expertise in video authoring. This work contributes a fundamental shift in generative video interfaces, demonstrating a powerful and accessible new way to craft visual stories.