How to Create a Cinematic AI Commercial

TL;DR

Build a cinematic AI commercial by creating consistent reference assets, using a Claude skill to turn scene descriptions into structured prompts, and generating each shot with Seedance 2.0 inside Higgsfield Collab. Test key assets together before locking them, keep element names consistent across tools, and use short test generations to catch visual problems without wasting credits.

Transcript

As a kid, I spent countless hours playing and watching football. Yes, football, not soccer. And I was obsessed with the commercials that ran in halftime breaks. I figured you needed a huge budget and a real studio to make something like that. Turns out you just needed one website. Hi, I'm Adil and today I will show you how you can do the same using... Read More

Key Insights

• Asset consistency is established before scene generation by creating character sheets, product views, locations, and prop references. Showing the soda can from the front, back, and top gives the video model more visual information and reduces the risk of inconsistent or invented product details.
• Soul Cinema is used for character and location references because it can preserve facial features across different angles and produce images resembling cinematic stills. The creator supplies 20 personal images for the avatar, then generates multiple character-sheet options before selecting and locking one outfit.
• The location reference determines much of the final video's texture and lighting because the video model draws those qualities from the supplied image. The selected New York street uses an anamorphic look, shallow depth of field, and film grain to establish a wide, less digitally clean appearance.
• Asset testing is a required checkpoint before production begins. The creator combines the chosen character and street in a five-second Seedance 2.0 test, asks for dynamic camera movement and a Dutch angle, and confirms that the character, location, and low camera angle work together.
• Visual continuity depends on designing related characters and props as parts of the same world. The two robots share a human-scale futuristic style but differ through white-and-red versus black-and-red armor, chest numbers seven and nine, and a more villainous appearance for the rival.
• Claude reduces prompt-writing effort by converting short creative requests into detailed, structured prompts. It is used to describe robot character sheets, a metallic red futuristic football, and an electrically glowing goal before those prompts are submitted to Soul Cinema for visual exploration.
• The prompting framework works by giving Claude the script, every approved asset, and explicit labels for characters, locations, robots, and props. This context allows the uploaded skill to transform plain-language scene descriptions into production-ready prompts without repeatedly rebuilding the project's visual definitions.
• Consistent element naming connects Claude's prompts with Higgsfield Collab. Each uploaded reference, including the civilian character, city location, soda can, robots, football, and energy-field goal, receives the same name used in Claude so the intended assets can be identified during generation.
• More videos from this channel:
  • How to Build and Publish 3D Games With AI
  • How to Add Cinematic 4K AI VFX to Real Footage
  • Is Seedance 2.0 4K Worth It for AI Filmmaking?
• More from this channel: Higgsfield AI

Questions & Answers

Q: How do you create a cinematic AI commercial?

Create the commercial through three stages: build consistent visual assets, prepare structured prompts, and generate the scenes. Make reference sheets for the product, characters, location, robots, and props first. Give the script and approved assets to Claude, then use its prompting framework to prepare Seedance 2.0 instructions. Finally, upload the references as consistently named elements in Higgsfield Collab and generate each shot.

Q: How can you keep an AI-generated product consistent across scenes?

Create a product sheet that shows the item from several useful angles before generating any video. In the demonstrated workflow, GPT Image 2 receives the soda can image and produces front, back, and top views. Those views give the video model a fuller representation of the can, helping it preserve the product's appearance instead of inventing unseen details in different shots.

Q: How do you create a consistent AI character from your own likeness?

Upload multiple photographs that show your face under different angles and lighting conditions, then use them to create a reusable avatar. The creator supplies 20 personal images to Soul Cinema and requests a two-panel character sheet with a full-body view and facial close-up. Multiple outfit variations are generated, compared for facial details, skin tone, hair, and overall suitability, then one version is locked.

Q: Why is the location image important in AI filmmaking?

The location image supplies textures, lighting, and environmental qualities that influence the generated footage. A sloppy or plastic-looking reference can therefore weaken every later shot, even when the video prompt is strong. The workflow uses a cinematic New York street reference with an anamorphic feel, shallow depth of field, clear daylight, and film grain, then tests it with the main character before approval.

Q: How should key AI filmmaking assets be tested?

Combine the most important assets in a short video test before committing to full production. The creator places the selected character into the chosen street, describes the action and emotional context, and requests dynamic camera movement with a creative angle such as a Dutch angle. A single five-second Seedance 2.0 generation is enough to evaluate character compatibility, location quality, and cinematic framing before locking both assets.

Q: How does the Claude prompting skill improve the workflow?

The Claude skill packages the creators' recent lessons about writing effective generation prompts into a repeatable framework. After it is uploaded through Claude's customization interface, the user provides the script, approved reference images, and descriptions of every named element. The skill can then convert plain-language scene requests into structured Seedance 2.0 prompts, reducing confusing revisions and unnecessary credit use.

Q: How do you design multiple robots that belong in the same visual world?

Keep their underlying format and overall design language similar while changing a few identifying features. The workflow requests front-and-back character sheets for both human-scale robots. One uses white-and-red armor with number seven, while the rival uses black-and-red armor with number nine. Several results are compared, and options with unsuitable lighting, helmets, proportions, or unclear numbering are rejected before selection.

Q: Why should element names match between Claude and Higgsfield?

Matching names create a clear link between the assets described in the prompt and the references uploaded to the generation workspace. The creator labels the civilian character, city location, soda can, two robots, football, and futuristic goal in Claude, then uses those exact labels when adding the images as elements in Higgsfield Collab. This helps the scene instructions refer to the intended visual resources consistently.

Summary & Key Takeaways

• The workflow begins by creating reusable references for every important visual element. GPT Image 2 produces a multi-angle product sheet, while Soul Cinema generates the human character, location, robots, football, and futuristic goal. These references help maintain recognizable designs, textures, lighting, and proportions throughout the commercial's separate shots.

• The prompting stage uses a downloadable Claude skill containing lessons about crafting effective generation prompts. The creator uploads the script and all approved assets, then supplies clear names and descriptions for each element. This framework converts plain-language scene ideas into more structured Seedance 2.0 prompts and reduces repeated, credit-consuming experimentation.

• Scene generation takes place inside Higgsfield Collab, where the approved assets are added as named elements. Their names match the labels given to Claude, creating a consistent connection between the prompting framework and the generation environment. The complete project includes 12 scenes covering transformations, robot football, a villain comeback, and a product pack shot.