AI Ad Creative
What to create, with which model, and how many tries it takes to make a usable ad asset.
Compare AI image and video generation models at the shot level for ad production. Use same-condition, multi-run data to judge whether an asset is usable, the steps it still needs, the reroll cost, and the risks. Live campaign metrics (CTR/CVR) require separate distribution tests.
4-second shot as the basic unitSeparates ad production fit from ad performanceDoes not claim performance without live data
Direct ad-task comparisons
Cross-model comparisons of scenes likely to be used in ads: ramen, coffee, cosmetics, etc.
videoDirect ad taskコーヒーを注ぐ手元
カフェ飲料hookproduct_demo
3-Run検証済みFull benchmark data — Japanese
videoDirect ad taskスニーカーの浮遊ショット
アパレルスニーカー・靴product_hero
3-Run検証済みFull benchmark data — Japanese
videoDirect ad task化粧品ボトルの回転ショット
化粧品美容product_heroproduct_demo
3-Run検証済みFull benchmark data — Japanese
videoDirect ad taskラーメンを食べる人物 I2V 動画比較｜飲食・食品広告で使えるモデルは？
飲食店食品メーカーhookproduct_demo
3-Run検証済みFull benchmark data — Japanese
Supporting asset / capability comparisons
Backgrounds, people, product integrity, camera control, and other elements usable as one ad cut.
What this section verifies
- Whether the asset can be used in an ad (instruction compliance, product integrity, motion naturalness, Japan-market fit)
- Number of retries and effective cost per usable shot
- Unverified elements and additional steps needed for a finished ad
- Model strengths/weaknesses by industry, channel, and in-ad role
- Live campaign metrics such as CTR, CVR, CPA (planned after partnerships)
- We do not generalize 4-second shot results to finished ads
- AI-generated assets are disclosed; rights/trademark checks are noted when real products/brands appear
