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How to make a 6-minute AI explainer video from a short prompt

Scenema Team · · Updated October 8, 2026
Contents: What you will learn

Most AI video tools can produce an impressive fifteen-second clip, but past thirty seconds the wheels tend to come off. Characters drift into strangers by shot five, and the narrator’s voice can swap timbre mid-sentence. The hard part is making dozens of separate generations feel like one continuous film.

This tutorial walks you through a working solution. The finished piece is a six-minute Vox-style animated explainer on the financial mechanics of the 2026 FIFA World Cup. You will see how it was built in Scenema from a single 79-word prompt and one style-preset selection, in about fifteen minutes total. All fifty-four shots share the same recurring character, the same narrator voice, and the same paper-collage aesthetic, and nothing was hand-stitched afterward.

The finished six-minute explainer, best watched with audio on.

What you will learn

  • How Scenema turns a short prompt into a long-form explainer video.
  • Why the same character stays visually consistent across fifty-four shots.
  • How to reproduce the piece from scratch, in about fifteen minutes.

For readers who prefer video walkthroughs, the full YouTube tutorial covering the same ground is below.

Video walkthrough of the same build, about 13 minutes.

The two inputs

Total user input for the finished six-minute film was a 79-word content prompt and a single style-preset selection.

The prompt, verbatim

A dry, clinical, and data-driven documentary-style video exploring the financial
mechanics of the FIFA World Cup. The video should present specific statistics
regarding ticket prices relative to minimum wage, FIFA's non-profit status and
tax advantages in Zurich, total revenue projections for the 2023-2026 cycle, and
the multi-billion dollar broadcasting rights deals with networks like Fox,
Telemundo, the BBC, and ITV. The tone should be objective, analytical, and
critical, focusing on the disparity between corporate profits and the average
worker.

The style preset

The style preset selected was Vox Explainer, which ships with a canonical palette (aged newsprint tan and charcoal black, punctuated by a single aggressive hot-red accent for key data and warnings) and a canonical motif vocabulary (halftone cutouts, staggered stat numbers with overshoot springs, layered paper dioramas, ALERT WASH transitions on ideologically loaded moments). The preset sets the look, and the content came entirely from the prompt above.

Inside the finished project

The screenshot below shows the project as it looked in Scenema after generation, with the shot strip, the keyframe and video prompts for each shot, and the character reference images this article describes. Everything in it is what Scenema produced from the two inputs above.

Project view in Scenema for The 13 Billion Dollar Feature, showing the finished video alongside the shot strip, keyframe prompt, video prompt, and character reference images.

The project view in Scenema after generation.

What actually held that usually does not

Character consistency across fifty-four shots

A small halftone-cutout figure labelled the New Jersey worker appears in roughly a third of the shots, from the opening tower of $10,990 tickets (the reported Category 1 price for the final at one point in the sale) to the closing bench-under-a-floating-stadium shot. Every appearance keeps the same silhouette, the same tired posture, the same halftone rendering, the same faded newsprint palette. The recurring stadium, the Aramco logo, and the category 1 ticket all hold the same way.

Reference images are generated once when the storyboard identifies each recurring entity. Every downstream shot that mentions the entity via its @tag receives those reference images as generation inputs. Mid-project, the model only ever places a known character into a new composition, guided by those reference images. For a full technical breakdown of the reference-image approach and why other consistency techniques fail past thirty seconds, see how to keep character consistency in AI video generation.

Single voice across the full six minutes

The narrator delivers all 1,100 words of the script in a single continuous voice with no swap, no drift, and no perceptible re-recording seam. This holds because Scenema generates the entire narration in a single pass through one voice model with one voice ID, rather than generating scene by scene and stitching the chunks. Long-form voice generation holds up well when the pipeline treats the narration as a single utterance.

Style coherence, injected at the shot level

The paper-collage-on-aged-newsprint aesthetic is a storyboard-level constraint, set before any shot is written. When the style preset is selected up front, the Director commits to concrete palette and motif language in the storyboard. That language is then injected into every single per-shot prompt written by the director agent downstream. Every keyframe prompt starts with the same palette instructions and the same motif vocabulary. There is no room for a shot to reinvent the visual language, because the shot prompt itself cannot be written without inheriting the preset’s language verbatim.

Under the hood

The pipeline runs in one direction, so nothing later in the chain informs anything earlier.

1. Narration script

Generated first, because everything downstream is driven by it. The Director reads the input prompt, considers the style preset, and produces a full narration script grounded in the actual FIFA financial numbers cited in the prompt, running about 1,100 words across eleven paragraphs.

2. Storyboard

Informed by both the narration and the style preset. Includes concept, palette, motifs, and an eight-act structure. You have the option to review the storyboard before any shot design starts. This is the recommended entry point for creative direction.

3. Shot list

The director agent takes the eight acts of the storyboard and the eleven narration paragraphs and decomposes them into fifty-four individual shots. Each shot has a specific piece of narration it is meant to illustrate, a duration derived from the pace of that narration text, and a placement in one of the eight acts.

4. Character reference images

Reference images are generated before any per-shot keyframe. The tool identifies four recurring entities (worker, stadium, Aramco logo, category 1 ticket) and generates one canonical reference image per entity. These references become reusable inputs for every downstream keyframe that features the entity.

5. Per-shot keyframes

Every shot receives a director-written keyframe prompt combining subject, framing, lighting, and mood. Any entity appearing in the shot is referenced by its @tag, which attaches the corresponding reference image to the generation.

6. Per-shot videos

Each shot’s video is generated from the corresponding keyframe plus a director-written video prompt describing camera and subject motion.

7. Narration audio

Generated in one continuous pass, using a single voice model and voice ID for the entire six minutes.

8. Final export

All fifty-four video clips, the narration audio, and any transitions from the shot list are muxed into a single mp4.

Reproduce it yourself

Starting from the same prompt, Scenema re-derives the storyboard, shots, and characters. Your run will produce a video in the same shape and style, with natural variability in specific compositions and characters.

Building from scratch is the same three steps used to produce the finished piece.

  1. Type a short prompt describing what the video is about and what tone it should carry.
  2. Select a style preset from the preset library.
  3. Review the storyboard when it appears, and ask the Director for any changes.

Everything after that runs automatically until the mp4 lands in the export tab, about fifteen minutes end to end. The Studio shows a live credit estimate before every generation so you always see the cost before you spend.

What broke, and what is still hard

The Vox preset is opinionated

The preset produced a highly coherent aesthetic across all fifty-four shots. However, its canonical motifs (halftone cutouts, hot-red ALERT WASH transitions, staggered stat number entries) do dominate the visual grammar in a way that is instantly recognizable. If you want a more restrained aesthetic, pick a different style preset from the library, or bring your own reference and register it as a custom style in Scenema.

Character identity holds at the silhouette level

The recurring worker cutout is unmistakably the same character across all his appearances. A viewer who freezes on individual frames will see minor variation in the halftone dot pattern from shot to shot. That level of variation is fine for stylized aesthetics like this one and less forgiving for photoreal work, which is why picking a stylized preset matters most when character continuity is critical.

Visual continuity between shots is the real open problem

Narrative continuity is handled: The narration flows, the shot list follows the story, and the shots line up with the beats they illustrate. What is not yet handled is true visual continuity between adjacent shots: the ability for shot N to end on a specific frame and shot N+1 to begin from that same frame, so the camera or subject motion carries across the cut. That requires first-frame-last-frame conditioning in the video model, which is an active area we are exploring for a future Scenema release. In the meantime, edits between shots read as clean cuts rather than continuous motion.

Where to go next

The FIFA piece is one worked example, and any subject you can describe in a short prompt will produce a comparable result through the same three steps.