How Should You Choose AI Video Inputs From a Product Catalog?

Rank Images by Product Visibility and Cleanliness

Ecommerce catalogs contain plenty of images, yet only a portion of them make dependable inputs for AI video. Choose assets that show the product clearly, preserve its proportions, and give the model a stable visual reference.

Start by ranking images on two practical dimensions: how much of the product is visible and how cleanly it is presented. A crisp, front-facing hero image usually produces more reliable video than a busy lifestyle shot where the product occupies one small corner.

• High priority: hero images with a full product view, clean edges, even lighting, and a simple background.

• Useful secondary inputs: alternate angles that reveal shape, finish, scale, or functional details.

• Lower priority: cropped images, heavily retouched composites, and images with prominent badges or text overlays.

• Exclude: thumbnails, duplicate color swatches, low-resolution images, and shots where the product is partly hidden.

A furniture retailer launching lamps and chairs could select one hero image per SKU, then add a side angle for chairs and a close-up showing the lamp shade or material finish. Those inputs give the generation system clear cues about silhouette, color, and construction.

Use Titles and Attributes as Focused Creative Guidance

Product data helps video generation when it clarifies the item shown in the image. Keep the request close to the details a shopper would use to understand the product: product type, material, color, size, style, and intended setting.

A title such as “Aster Walnut Dining Chair” provides a strong starting point. Attributes can add “solid walnut frame,” “woven seat,” “mid-century style,” and “dining room use.” That information supports a prompt such as: “Show this walnut dining chair in a warm, modern dining room with a slow camera move.”

Long descriptions often contain shipping notes, keyword stuffing, care instructions, and internal taxonomy. Filter those fields before sending them into a generation request. Video prompts work best when the catalog data describes visible facts and a small number of creative choices.

Build a compact prompt template around the fields you trust. For example:

• Product name and category

• Color, material, and defining features

• Desired scene or use case

• Camera motion and ad format

Flag Categories That Need Human Photography

Some products need a stronger photographic foundation before they enter an AI video workflow. Items with complex fit, regulated claims, reflective surfaces, or fine construction details benefit from carefully planned source imagery.

Apparel needs consistent on-body photography when fit and drape drive the purchase. Jewelry, glassware, and polished metal need lighting that accurately shows reflections. Food, cosmetics, and products with compliance-sensitive packaging need source images that preserve labels and claims.

Create a catalog flag for categories that require approved hero photography, multiple angles, or a human review before generation. This keeps low-confidence assets from entering automated creative flows and gives your team a clear production queue.

For the furniture retailer, lamps with translucent shades may need a dedicated lit image, while upholstered chairs may need close-ups that show fabric texture. Those images help generated clips retain the details shoppers use to judge quality.

Create an Input Readiness Score for Every SKU

An input readiness score turns asset selection into a repeatable workflow. Score each SKU from one to five for product visibility, image quality, background cleanliness, attribute completeness, and category risk.

A lamp with a clean hero image, accurate finish data, and an approved lifestyle reference might earn a five. A chair with only a tiny marketplace image and incomplete material data might earn a two and move into an asset-improvement queue.

Use the score to route products automatically. High-scoring SKUs can enter catalog-driven video generation, medium-scoring products can receive a human review, and low-scoring products can wait for better photography or metadata.

With Protoface, teams can send approved catalog images and focused product data through a hosted API, then pay per generated second as videos are created. That makes it practical to test video generation across a collection while keeping the input rules under your control.

Reliable catalog video starts with reliable inputs. A disciplined selection process gives each generated clip a clearer product reference, stronger merchandising value, and a better chance of looking ready for a product page, ad, or social placement.