生成首段 AI 影片前，如何設計盲評流程
Design a Blind Review for AI Video Before You Generate the First Clip
文章提出一套尚未實測的 AI 影片模型盲評流程，建議先固定比較任務與選片規則，再以匿名標籤評分。流程將指令遵循、手部與物件接觸、角色一致性、動作完成度及主觀偏好分開記錄，並在評分後揭示模型身份和交付限制。文章沒有進行實驗或產生評分結果，單一開盒任務亦不能支持影片模型的絕對排名。
A model name can influence an evaluation before the video starts. Hiding that name is useful, but it does not fix unequal prompts, retries or selection rules.
This is an untested evaluation protocol adapted from FablessBridge's production notes. We have not generated or scored clips for this proposed comparison, and there is no winning model or measured improvement to report.
1. Freeze the task and selection rule
Use a narrowly observable task. The source proposes one person opening a box on a desk and then removing their hand. Check instruction adherence, hand–box contact, character consistency and completion of the action separately from visual appeal.
Before generation, record duration, aspect ratio, audio, input image, model version and access route. Document settings that cannot be matched. Decide whether you will compare the best output from each model or every output from a fixed number of attempts. Do not quietly grant one model extra retries.
2. Separate the identity map from the review package
Label clips A, B and C. Keep the model-to-label mapping separate until scoring is complete. Inspect filenames, player labels and surrounding descriptions for identity clues. A visible service watermark makes blinding incomplete; record that limitation.
If an evaluation copy needs cropping or other processing, preserve the original and document the changed area. Processing can change the very quality or framing being judged. Record presentation order and rotate it so the same model is not always shown first.
3. Use an observation sheet, not a single preference score
This blank template turns the proposed checks into fields that another reviewer can reuse. It contains no test results.
| Field | What to record |
|---|---|
| Clip label and timestamp | Which output and moment the observation concerns |
| Instruction adherence | Whether the requested action is visible |
| Contact and geometry | Hand–box contact and changes in object shape |
| Character consistency | Whether the person stays consistent across the shot |
| Action completion | Whether the hand actually leaves the box |
| Preference | Mood, pace and color, kept separate from defects |
| Review limitation | Identity clues or prior knowledge of production conditions |
Save individual assessments before the group discussion. If a reviewer changes their mind after discussion, keep the original assessment as well. The source's examples of useful observations include a box retaining its shape and fingers appearing to increase when a hand moves away; these are illustrative observations, not findings from a completed test.
4. Reveal the model, then assess delivery constraints
After scoring, open the mapping and add attempt counts, wait time, cost and available features. Explain how many failed outputs were excluded. A clip preferred visually may still be unsuitable for the project's deadline or budget.
Report the result within its task and conditions. A comparison of one box-opening task cannot establish an absolute ranking of video models.
Before using this protocol
Check that the selection rule was fixed in advance, unmatched settings are visible, original files remain available and individual assessments have not been overwritten. These checks make the proposed review easier to inspect; they do not prove that it is unbiased or that a particular model is best.
If you are planning a business video, define the required actions and delivery constraints before choosing a model. You can explore FablessBridge's production examples, visit the official website, or discuss a production brief. Business contact: fabless-bridge@shian.space.
Source: FablessBridge's proposed blind-evaluation workflow. This English adaptation was written using AI from that source. No experiment was performed for this article.
來源：DEV Community · dev.to