DEV Community: 佐藤光

A Seven-Stage Workflow for Completing an AI-Assisted Video

佐藤光 — Wed, 14 Oct 2026 16:46:18 +0000

A folder of impressive clips is not yet a film. The sequence, text, sound and delivery format still need to work together.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Repair the actual failure

A scissors problem was addressed in the source image. A box handoff was split into starting and ending states. An HTML animation jitter was traced to layout sorting on every frame and changed to a one-time sort. These are different failure modes; repeated generation would not fix the layout code. Locate the defect before choosing a correction.

Approve work in stages

We worked through brief, representative images, motion, HTML text and animation, music and effects, review, and export. Image candidates established the cast and visual direction before motion. Exact on-screen text was handled separately. Stage approvals made it possible to identify whether a defect belonged in an input image, the generated movement or the edit.

Use one complete project as the unit

Our fictional Yoine launch sample is a 76-second film about a smart eye mask before manufacturing. The record includes 68 generated images, 37 clips and seven versions. Its story depicts a founder testing ideas with pages and ads; it is a fictional sample, not evidence of a live campaign or sales outcome.

Checks to carry into the next project

Approve representative images before generating all the clips.
Locate whether a failure belongs in input, motion or editing.
Review each delivery ratio as its own composition.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Adapted from the original FableBridge production note (Japanese). Model specifications, estimates, prices and schedules are records from the source date, not guarantees for a new project. The source includes further conditions and references.

FableBridge, operated by Shian Co., Ltd. in Tokyo, produces product and service videos, landing pages and advertising campaigns. Share your audience, placement, preferred length and timeline through our consultation page or email fabless-bridge@shian.space. Explore our work on the official website.

Review AI Video in Five Stages Before Polishing Details

佐藤光 — Wed, 14 Oct 2026 16:40:33 +0000

When a first draft feels wrong, decide whether the concept is wrong before spending time polishing its details.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Check continuity and exactness separately

We used 24 storyboard images and 23 Kling motion intervals. Static logos improved exactness but weakened movement, so the edit had to recover continuity while retaining correct text. This is a useful split in review: inspect how shapes connect across the cut, then inspect product words and marks. One attractive frame cannot establish either across the whole sequence.

Show the solved state

The storyboard needed clearer scenes of the problem being resolved, including the 10-15 and 25-30 second ranges. A film can describe a problem accurately while failing to show the benefit. Review the storyboard for the intended change in the viewer's understanding before generating all the motion.

Agree on the direction first

For a 30-second Ekusid service video, the first three concepts used people. The chosen direction moved to a people-free paper-and-UI treatment. That is a concept decision, not a minor visual correction. A client should say which audience and message the draft misses, then identify the parts worth preserving.

Checks to carry into the next project

Separate a direction change from a detail correction.
Check whether the benefit is visible in the storyboard.
Give feedback with a time range and a reason.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Use Claude Code to Orchestrate a Bounded Video Workflow

佐藤光 — Wed, 14 Oct 2026 16:40:06 +0000

Make it more natural is a creative intention. A timestamp and a concrete defect make it a reviewable production instruction.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Make feedback local

We compared old and new versions and gave notes with seconds: what happened, what should remain, and what should change. If an instruction could be read in several ways, ask the coordinator to restate its interpretation before execution. This helps avoid a broad rewrite when only one cut or one line of narration needs attention.

Bound the paid work

For the fictional 76-second Yoine sample, the generation estimate was about USD 23 against a USD 25 budget, excluding labor and Claude costs. A script should estimate before submitting, record each receipt and stop at the agreed limit. A network timeout should not trigger an unexamined paid retry. Keep costs attributable to a particular attempt and version.

Give the coordinator a defined role

We used Claude Code to organize requirements, call image and video APIs through scripts, build text and motion in HTML, arrange audio and export versions. It was the coordinator of those steps, rather than the video generation model itself. People still chose the creative direction and judged the output.

Checks to carry into the next project

Define deliverables and a spending limit.
Use timestamps and specific defects in revision notes.
Name the person who will watch and listen to the final export.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Assemble 24fps Clips into a 30fps Remotion Composition

佐藤光 — Wed, 14 Oct 2026 15:23:58 +0000

Generating the shots was one stage. Making the product name, transitions and sound land on the intended frames was another.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Place sound on the same timeline

Music and effects were placed at frame positions, with fades where needed. The final mix in this record measured about -17.5 LUFS and -2.7 dB peak; these are project measurements rather than universal loudness targets. Review speech and music together after export, as a correct source track does not establish a correct final mix.

Keep copy and motion editable

We used Composition for the complete timeline and Sequence for the cuts, with exact text and diagrams rendered as code. Letter animations were offset by three frames. Where clip duration differed from the allotted cut, playback rate was adjusted from the source duration and cut length. Preserve the approved wording outside the generated footage so revisions do not require a new video generation.

Define the output timeline

Our fictional coffee sample uses five cuts in a 30-second film. Input clips were 24fps at 1080p; the output composition was 30fps and 900 frames, exported as H.264. The recorded environment was Node 24, Remotion 4.0.532 and Apple Silicon. Fixing the duration and frame rate first made the other timings explicit.

Checks to carry into the next project

Fix duration and fps before timing the layers.
Keep approved copy in an editable layer.
Inspect the exported file with audio enabled.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Review a Generated Clip Before Paying for Another Attempt

佐藤光 — Wed, 14 Oct 2026 15:23:23 +0000

A disappearing hand made the end of one shot unusable. It did not make every frame of that shot unusable.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Separate generation from exact text

We kept product names and logos out of the generated image wherever exactness mattered, then added them in the editing stage with Remotion. Generated frames can look convincing while containing incorrect marks or words. Review those separately from camera motion and lighting, and keep the final copy as an editable layer.

Two errors, two responses

One shot moved in the wrong direction at a mailbox, so we generated it again. Another had a hand disappear at about 3.8 seconds. We retained the usable first 3.6 seconds and adjusted its speed to fit the edit. Those were different fixes for different errors, not a rule to slow down every problematic clip.

Select images before motion

For a fictional 30-second coffee subscription sample, we generated 15 Soul 2 images and six five-second Kling 3.0 Pro clips through Higgsfield. The estimated generation total was about USD 5.1, within a USD 10 budget. That does not include production labor. Comparing image candidates first helped us decide which compositions deserved a video attempt.

Checks to carry into the next project

Identify the first unusable frame.
Decide whether the error is in the input, motion or edit.
Estimate the next attempt before sending it.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Keep the Composition Stable Across Sketch, Pencil and Generated Film

佐藤光 — Wed, 14 Oct 2026 15:16:50 +0000

The transition worked because the drawing and the film-like image described the same composition.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Review inputs and transitions

We reviewed 25 images before animating them, including faces, a car emblem and a creature design. Motion then revealed different problems: a residual white car and a mismatch in the road at the cut needed attention. An image review cannot replace a motion review. Inspect the first and last frames as well as the attractive middle frame.

Give each tool one job

Code drew the lines, Nano Banana Pro produced the still images, Kling v3 added movement, and ElevenLabs supplied music and effects. This was a fictional production sample rather than footage of real events. It shows how generated assets and editing can be combined; it does not imply that one prompt produced the completed film.

Build a shared composition

Our 37-second in-house sample follows a fictional notebook hero through rooftops, a chase, a creature and space before returning to dinner. We started with simple line drawings, generated film-like images from those compositions, then made pencil-shaded versions. Keeping the layout recognizable helped each visual style read as the same scene.

Checks to carry into the next project

Show a rough sequence, even if it is a stick drawing.
Name the details that must remain consistent.
Review transitions and endpoints after generating motion.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Ship an AI Video with Display and Measurement Requirements

佐藤光 — Wed, 14 Oct 2026 15:16:20 +0000

An MP4 is a delivery file. Whether it works on a landing page or in an ad depends on decisions made around that file.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Connect the version to the result

Give each video version a clear identifier and connect it to the campaign URL or UTM values. Decide whether success means a consultation or another concrete conversion. If both pixel and server-side events are used, check deduplication so one inquiry is not counted twice. A playback count does not establish that the video generated qualified work.

Make the page usable before playback

For a landing page, prepare a poster image and decide when the video should load. Avoid loading every large clip immediately, especially below the fold. Test muted inline playback on the actual mobile page, and confirm that the page still communicates its offer before the visitor presses play. The video should support the page rather than delay its basic use.

Start at the placement

Choose aspect ratio, duration and safe areas for the intended placement. A horizontal landing-page film and a vertical social ad may need different compositions, not just different crops. Share the destination and launch date before storyboarding, then check the current requirements of the platform where the video will run.

Checks to carry into the next project

Name the page or ad placement.
Specify ratio, length and publication date.
Define an inquiry event before judging results.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Compare Video Models with the Same Image and Motion Brief

佐藤光 — Wed, 14 Oct 2026 15:10:14 +0000

The useful question was not which model won. It was whether the shot kept the coffee berries in frame while the morning light changed.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Include the delivery route and price

Through Higgsfield, the recorded five-second Kling 3.0 Pro estimate was USD 0.84 and the Seedance 720p estimate was about USD 2.31. The resolutions differ, and a provider route can change available settings and prices. Record input, prompt, route, model, resolution and estimate together. Recheck pricing when you run the next test.

Compare the actual outputs

In this test, Seedance made the light warmer and kept the red berries visible. Kling moved forward enough that the berries left the frame. The Kling output was 1920 by 1080, while Seedance was 1280 by 720. These were individual attempts; repeated generation can produce a different movement, so neither result establishes a universal ranking.

Describe the required shot

We used one image of a highland coffee farm and asked for a slow forward camera movement, drifting mist and growing sunrise light. Defining those visible changes made the output easier to judge than a broad request for a cinematic result. When commissioning a shot, name the subject that must stay visible as well as the movement you want.

Checks to carry into the next project

Write down what must remain visible.
Test the same image and movement with a small shortlist.
Compare revision effort as well as the quoted generation cost.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Check the Model ID Before Migrating a Veo Integration

佐藤光 — Wed, 14 Oct 2026 15:09:08 +0000

A model migration starts with an exact identifier. A product screen that only says Veo does not tell you which retirement notice applies.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Test the output contract

Record the provider, model ID, request settings and expected output before switching. Check resolution, duration, audio, download handling and estimated cost with a small test. In our Omni test, audio was generated with the video, so a workflow that creates music separately needed to mute or replace that track in editing.

Keep a small comparison reproducible

We tried the same input image and motion prompt with two Veo outputs and two Gemini Omni outputs. Those four clips document this production test, not a general benchmark. The Omni attempts took about 42 and 36 seconds, with preflight estimates of about USD 1.03 and USD 0.83 in the recorded conditions. These are observations and estimates, not a promise about later jobs.

Separate the API routes

In the official notices checked on October 14, 2026, the Gemini API Veo 3.1 preview models and gemini-omni-flash-preview had an earliest shutdown date of October 22, with gemini-omni-1.1-flash named as the replacement. The Vertex AI production -001 models had a separate retirement notice, no earlier than November 17. Check the current notice and the exact model ID before changing a production workflow.

Checks to carry into the next project

Record the exact model ID and API route.
Check the current retirement notice.
Approve a small output test before moving the rest of the project.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch this production clip (MP4)

Source and production inquiries

Prevent Duplicate Video Jobs with Receipts and Bounded Polling

佐藤光 — Wed, 14 Oct 2026 15:05:07 +0000

A slow video job should not automatically become a second paid job. The receipt matters as much as the request.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Persist the receipt

Save the request configuration and estimate before sending. Send a unique Idempotency-Key and immediately store the returned request_id and status_url. Poll the existing job until completed, then obtain the output URL. If a connection drops or a polling deadline expires, preserve the pending record instead of blindly submitting a new generation. Inspect the original job first.

Estimate before submitting

The recorded formula was ceil(height * width * seconds * 24 / 1024) / 1000 * 0.0214. It gave about USD 2.31 for a five-second 720p clip and USD 1.03 for 480p. The three runs totalled approximately USD 5.65 against a preflight budget of USD 8. These were recorded estimates; check the current price and actual invoice separately.

The scope of the run

On October 14, 2026, we used Higgsfield API to call bytedance/seedance-2.5/image-to-video. This was not a test of a ByteDance-operated API endpoint. Each output was five seconds at 16:9, with generate_audio set to false because sound would be produced separately. Provider routes affect the settings, queue and price.

Checks to carry into the next project

Record the preflight estimate and budget.
Persist the request ID before waiting.
Investigate an existing job before deciding to resubmit.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Watch the video (MP4)

Watch this production clip (MP4)

Watch the video (MP4)

Watch this production clip (MP4)

Watch the video (MP4)

Watch this production clip (MP4)

Watch the video (MP4)

Watch this production clip (MP4)

Source and production inquiries

Review Generated Narration as Text, Timing and a Final Mix

佐藤光 — Wed, 14 Oct 2026 13:32:53 +0000

A written line can be correct while its generated voice is wrong. It can also sound right alone and fail in the finished mix.

This implementation note records the inputs, failure modes and checks needed to make the workflow reviewable.

Review the combined tracks

An early version had only 16.5 seconds of singing. We rebuilt the song with vocals from 0:00 to 1:28 and lowered them by about 5 dB under narration instead of removing them. Ambience and effect placements increased from 17 to 27. Those were choices for this film, not targets for every mix. Listen for the role of each sound in the scene.

Keep two representations of the script

The Japanese word for product, 商品, sounded like shōpin instead of shōhin in one output. We kept the displayed subtitle in kanji and changed the speech input to the phonetic hiragana しょうひん. Maintain an approved display version and a pronunciation version, then listen to the generated audio. A correct input string is not proof of a correct recording.

A shorter take was not the better take

In our 90-second in-house cosmetics sample, one revision reduced six spoken lines from 15.5 seconds to 9.9 seconds. The pauses became compressed, so we returned to the previous version and reduced the emotional performance line by line. Tone and duration needed separate checks; a lower word count or shorter file did not establish a more natural result.

Checks to carry into the next project

Listen to narration without the picture.
Check it against the approved displayed words.
Listen to the exported mix with music and effects enabled.

Keep a reproducible record of the configuration and inspect the final exported artifact. An automation success message is not a content review.

Watch the production examples

These assets come from the documented project. Fictional in-house samples illustrate our process; they are not evidence of client sales results.

Listen to the audio (MP3)

Listen to the comparison audio (MP3)

Listen to the audio (MP3)

Listen to the comparison audio (MP3)

Listen to the audio (MP3)

Listen to the comparison audio (MP3)

Listen to the audio (MP3)

Listen to the comparison audio (MP3)

Listen to the audio (MP3)

Listen to the comparison audio (MP3)

Listen to the audio (MP3)

Listen to the comparison audio (MP3)

Watch this production clip (MP4)