AI Model Video
Upload the product first, then configure both model and scene to generate wearable, lifestyle-driven video creatives.
AI Model Video combines product, model, and scene into one video workflow. It is built for categories that need human presence, fit demonstration, or a stronger lifestyle layer than product-only video can deliver.
When To Use
Use this page when viewers need to see the product on a person or inside a more lifestyle-driven presentation.
It is especially helpful for apparel, shoes, accessories, and beauty workflows where persona and atmosphere strongly affect conversion.
Workflow
The most stable order is product first, then model choice, then scene choice, and finally video generation.
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Step 1: Upload Product Images
The product image remains the core subject and also affects how many additional model and scene references the page will allow.
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Step 2: Choose a Model Path and a Scene Path
Both model and scene support recommended, uploaded, and custom options. Use filters and modal pickers to narrow to the right templates before generating.
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Step 3: Set Parameters and Generate
After confirming the mode and output settings, generate the video and manage the result versions from the gallery.
How Model and Scene Pairing Works
The most common failure mode is not generation quality β it is mismatch between product type, model persona, and environment.
- Recommended models can be filtered by product-fit and persona attributes
- Recommended scenes can be filtered by product type, scene type, and keyword
- Uploaded references are best for owned or branded templates
- The page visually enforces the total-image cap across product, model, and scene assets
Best Practices
If you are testing audience expression, keep the product and scene stable while changing only the model variable. If you are testing lifestyle fit, keep the model stable and change the scene.
Once you get one usable version, Edit Again from the gallery is usually faster than rebuilding the whole request stack from scratch.
- Choose the persona first, then the environment
- Control variables so comparisons stay interpretable
- Winning videos can become reusable references for later image and video runs