A video-to-video editor has a different starting point from a text-to-video generator: the user already has a performance they want to keep. The interface needs to make clear what should change and what the source footage is meant to guide.
Our project, AI Genjutsu, focuses on character replacement using a source video and a reference image. This post shares a practical design checklist for that kind of workflow. It is not a model benchmark or a guarantee of frame-perfect preservation.
1. Separate the two inputs
The source clip supplies the performance: movement, timing, framing, and camera behavior. The reference image supplies the intended character appearance. Treating them as separate inputs makes the task easier to explain than a single upload box labeled “media.”
A useful interface gives each input its own label, preview, and validation message. If the reference image is missing, tell the user that directly instead of returning a generic generation error.
2. Validate media before requesting generation
An accepted file extension does not tell you whether a video meets a model's requirements. Duration, dimensions, frame rate, codec, and file size can each matter.
For developers, the useful distinction is between three states:
- Invalid: the file cannot be used and needs a specific explanation.
- Needs preparation: it can be adapted, but the user should understand what will change.
- Ready: the prepared inputs satisfy the selected workflow's requirements.
Client-side checks provide quick feedback. The server should still validate inputs before dispatching a paid job; a browser check is not a trust boundary. If preparation changes media properties, validate the prepared output too.
This is a suggested sequence, not an API specification:
choose video and reference
-> inspect media
-> explain any required preparation
-> validate prepared inputs
-> show a generation quote
-> request user confirmation
-> submit the job
3. Make the cost boundary visible
Selecting or preparing a file and submitting it for AI processing are different actions. A user should be able to tell which action spends credits.
Show the quote after the relevant inputs and output options are known. If the user replaces the clip or changes output quality, refresh the quote before allowing submission. On the server, verify that the confirmed quote still matches the requested job.
A disabled button should also explain what is missing: a reference image, a compatible video, or a current quote.
4. Give users a review checklist
“Generation completed” means an output is available. It does not establish that the edit is visually good. For a character replacement, useful review questions include:
- Does the character remain recognizable throughout the clip?
- What happens when the face turns or leaves the frame?
- Are hands, clothing, and subject boundaries consistent?
- Does the movement still follow the intended performance?
- Did the background change in a distracting way?
Start with a short, continuous shot and a clearly visible subject. A complicated shot with overlapping people makes it harder to isolate why an output looks wrong. Use footage and references you have permission to edit.
Where AI Genjutsu fits
AI Genjutsu is our independent web project for this source-video-plus-reference-image workflow. Generation uses paid credits; it is not a free rendering service. It is not affiliated with or endorsed by Higgsfield.
The product page explains the current upload requirements and workflow. The broader design question is useful beyond our project: how can an AI media interface help users understand the inputs, expected changes, and cost before they start a job?
What validation or preview step has saved your users the most failed generations?
Disclosure: this article was generated by an AI agent at the project owner's request, using the project's public product information. It has not been edited by a human.
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