cs.HCMar 10, 2026
SaveA Text-Native Interface for Generative Video Authoring
Organizations: Adobe Research, San Francisco, CA, USA · Adobe Research, Seattle, WA, USA
Abstract
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.
Figures & tables
Figure 1. Doki is a text-native video authoring interface that creates generative videos within a single document. (a) Users define reusable assets and styles with mentions and hashtags, (b) view and adjust inline previews, (c) access commands through a slash menu, (d) write paragraphs that compile into video shots, and collaborate with both (e) a conversational AI agent and (f) inline agent.
Figure 2. A comparison of interface paradigms. (a) “Bento Box” style interfaces distribute authoring across multiple, separate representations. (b) Doki’s approach uses a text-native canonical representation where the document serves as the primary interface.
Figure 3. Two basic example workflows in Doki. Alice: (1) define assets and shots with slash commands (2) write story and generate previews (3) create video shots; Bob: (a) prompt the sidebar agent for a draft (b) review the AI-generated draft (c) refine with inline AI agent.
Figure 4. Creating a shot in Doki. A new shot is inserted inline with a slash command, and the description that follows serves as its prompt. The system first generates a preview image, which can then be turned into a video clip. Users can click to expand them for playback and additional controls.
Figure 5. Writing consecutive shots within a single paragraph. Later shots inherit context from earlier ones, enabling continuity across a sequence. We achieve strong consistency between shots without repetitive context description in the Doki document.
Figure 6. Creating definitions in Doki. Users open the command menu with a / , select a type, and provide a name and description. Optionally they can add a visual definition for even better consistency.
Figure 7. Adding variants to a definition and shot. The user selects \inter Add Variants from the context menu by right-clicking on the image. Three variations will be added inline. The user can then select one variant by moving it to the first position.
Figure 8. Authors can apply definitions globally ( \inter #all \inter = ) or scope them to specific sections via headings. In the example, a scoped definition ( \inter #Cartoon ) transforms subsequent shots under the heading level into a consistent cartoon style.
Figure 9. Three types of inline agent actions. (a) “Enhance” increases the descriptiveness of the selected text. (b) “Create Definition” converts the selection into a reusable definition. (c) “Custom Request” applies an in-context edit based on user instruction.
Figure 10. Doki’s shot generation pipeline. First, the user’s raw prompt and the document context is passed to reference resolution module to create a structured prompt and gather relevant visual reference images from the definitions. Then, the prompt rewriter rewrites and polishes this structured prompt for image and video generation. Doki first produces a static preview image and then uses that image as a the first frame to generate the final video clip.
Figure 11. Example frames from videos created by participants in our diary study. The videos demonstrate the breadth of video types Doki supports. Each row corresponds to a single video, shown left to right through four representative keyframes.
| Id | Role | Video Creation Freq. | GenAI Use (Text) | GenAI Use (Image) | GenAI Use (Video) | SUS |
|---|---|---|---|---|---|---|
| 1 | UX Designer | A few times a year | A few times a year | Monthly | A few times a year | 87.5 |
| 2 | Filmmaker | Daily | Daily | Daily | Daily | 77.5 |
| 3 | Product Designer | Weekly | Rarely or never | Rarely or never | Rarely or never | 75.0 |
| 4 | Software Engineer | A few times a year | Daily | Weekly | Monthly | 90.0 |
| 5 | Animator | Daily | Rarely or never | Rarely or never | Rarely or never | 80.0 |
| 6 | Program Manager | A few times a year | Daily | Monthly | A few times a year | 72.5 |
Table 1. System Usability Scale (SUS) scores and subscales (Usability and Learnability) across participant roles, video creation frequency, and generative AI tool use.
| Id | Age | Gender | Role | Video Types | Video Editor Use |
|---|---|---|---|---|---|
| 1 | 28 | M | UX Designer | Professional films | DaVinci Resolve, GenAI tools |
| 2 | 32 | M | Filmmaker | Social media, Professional films | Adobe Premiere Pro, Final Cut Pro |
| 3 | 24 | F | Product Designer | Social media, Personal story or vlogs | Adobe Premiere Pro, iMovie, CapCut |
| 4 | 57 | M | Software Engineer | Personal Story or vlogs, Music video | Adobe Premiere Pro |
| 5 | 28 | F | Animator | School projects, Work-related content, Professional films | Adobe Premiere Pro |
| 6 | 37 | F | Program Manager | Social media, Personal story or vlogs | CapCut |
Table 2. Participants information including demographics, roles, video practices, and generative AI tool usage.
Figure 12. Additional features in Doki: (1) Preview video player shows the generated video with shot segments. (2) Settings panel lets users select AI models, toggle cost/debug options, access tutorials, and manage projects. (3) Cost monitor tracks generation usage and expenses across images and videos in real time. (4) Download menu provides output in video, clip, or JSON formats.
Figure 13. Doki interface usage analytics. Left: Total cost by session duration, with point color indicating regenerations. Cost data are available for 35 of 50 sessions, as some sessions were logged under ongoing projects rather than new ones as instructed. Right: Distribution of per-minute activity rates for images, videos, and regenerations. Images show the highest median rate, followed by videos, with regenerations least frequent.