| name | ad_video_create |
| description | Create ad-ready product video from product images, with or without character/subject images. The workflow leverages AI-powered image composition, scene understanding, and video generation. Video prompts should follow commercial shot language—visual hooks, product presence, hero shots, detail showcase, function expression, and dynamic visuals. |
Workflow Architecture
Phase 1: Asset Preparation & Analysis
Input Requirements:
- Primary Asset (Required): Product image (e.g., cat tower, furniture, gadget)
- Character/Subject Asset (Optional): Supporting character image (e.g., pet, person, lifestyle element)
- Audio Asset (Optional): Background music file (MP3 format)
Process:
- Asset Discovery: Scan working directory for available assets
- Media Comprehension:
- Activate
media_comprehension skill
- Analyze product image to understand:
- Product features and characteristics
- Color palette and material textures
- Suitable environment context
- If character image exists, analyze its attributes (appearance, pose, mood)
Phase 2: Character Generation (Conditional)
Trigger Condition: No character/subject image provided
Process:
- Based on product analysis from Phase 1, determine appropriate character type:
- For pet products → Generate pet character (matching product target audience)
- For home goods → Generate lifestyle character or scene element
- For tech products → Generate user persona or usage scenario
- Call
image_generator with detailed prompt:
- Character attributes aligned with product positioning
- Pose and expression suitable for composition
- Style consistency with product aesthetic
Output: Character image ready for composition
Phase 3: Image Composition with Environment
Objective: Create a realistic advertisement scene combining product + character + environment
Key Requirements:
- Single Character Constraint: Ensure only ONE character appears in final composition
- Environment Background: Must include realistic home/lifestyle setting, not plain white background
- Natural Integration: Character should interact naturally with product
Process:
- Prepare input images:
- Product image (original or compressed if >50KB)
- Character image (from Phase 2 or user-provided)
- Call
image_generator with composition directive:
{
"content": "Compose [character description] with [product description] in [environment setting].
Requirements:
- Only ONE character in the scene
- Realistic home environment (floor, walls, natural lighting, plants, furniture)
- Natural interaction between character and product
- Professional product photography style",
"info": {
"image_urls": ["product.jpg", "character.jpg"],
"size": "1328x1328",
"guidance_scale": 4.5-5.0,
"num_inference_steps": 30-35,
"watermark": false,
"output_path": "./composed_ad_image.png"
}
}
Output: High-quality composed advertisement image with environment
Phase 4: Video Generation
Objective: Transform static composition into dynamic advertisement video
Shot & visual language (required): Across the ~10s runtime, the motion and camera work should cover these elements where applicable (not necessarily every second, but the final cut should feel like a mini commercial, not a single static pan):
| Element | Meaning |
|---|
| Visual hooks (视觉因子) | Strong focal points, contrast, color, light, or composition that hold attention |
| Product presence (产品出现) | Clear establishment of the product in frame—viewer knows what is being advertised |
| Product / hero shots (产品镜头) | Dedicated beats where the product is the clear subject (center framing, readable silhouette) |
| Detail showcase (细节展示) | Close-ups or slow emphasis on materials, texture, craftsmanship, or key parts |
| Function / benefit expression (功能表达) | Motion that implies use, outcome, or core selling point (interaction, before/after feel, problem–solution rhythm) |
| Dynamic visuals (动态视觉) | Varied motion: camera (push, pan, subtle orbit), parallax, light shifts, or subject micro-movement—avoid one flat move for the whole clip |
When writing video_diffusion prompts, spell out which of the above appear in sequence (e.g. establish product → detail → function beat → dynamic wrap). If the source image is character-heavy, still reserve beats for product-first shots.
Audio Handling Strategy:
Case A: User-Provided Audio (MP3 exists in directory)
- Generate video WITHOUT audio first via
video_diffusion:
{
"content": "Create dynamic advertisement video (mini-commercial pacing, ~10s):
- Visual hooks: strong focal points, light/color contrast where fitting
- Product presence: early establishment of the product in frame
- Product hero shots: beats where the product is clearly the subject
- Detail showcase: close-up or emphasis on texture/material/key parts
- Function expression: motion suggesting use, benefit, or core value
- Dynamic visuals: varied motion (camera push/pan/subtle orbit, parallax, light shifts, optional character micro-movements)
- Professional commercial quality",
"info": {
"image_url": "./composed_ad_image.png",
"resolution": "720p",
"duration": 10,
"fps": 24,
"output_dir": "./",
"sound": "off"
}
}
- Merge video with user's MP3 using FFmpeg:
ffmpeg -i generated_video.mp4 -i user_audio.mp3 -t 10 \
-c:v copy -c:a aac -b:a 192k \
-map 0:v:0 -map 1:a:0 -shortest \
final_ad_video.mp4 -y
Case B: No User Audio (Generate with AI audio)
- Call
video_diffusion with audio generation enabled:
{
"content": "Create dynamic advertisement video with suitable background music (mini-commercial pacing, ~10s):
- Visual hooks; product presence; hero product shots; detail showcase; function/benefit expression; dynamic visuals (varied camera and motion)
- AI-generated background music matching product mood
- Professional commercial quality",
"info": {
"image_url": "./composed_ad_image.png",
"resolution": "720p",
"duration": 10,
"fps": 24,
"output_dir": "./",
"sound": "on"
}
}
Output: Final advertisement video (10 seconds, 720p, with audio)
Best Practices
Image Compression
- Always check file size before reading images with
media_comprehension
- Compress if >50KB using PIL/Pillow:
from PIL import Image
img = Image.open(path)
if img.mode in ('RGBA', 'LA', 'P'):
img = img.convert('RGB')
img.save(output_path, 'JPEG', quality=85, optimize=True)
Prompt Engineering for Composition
- Be explicit about character count: "Only ONE [character type] in the scene"
- Specify environment details: Floor type, wall color, lighting direction, furniture elements
- Emphasize natural interaction: "Character naturally using/enjoying the product"
- Request professional style: "Product photography style, commercial quality"
Video Motion Guidelines
- Shot vocabulary: Align prompts with visual hooks, product presence, hero product shots, detail showcase, function expression, and dynamic visuals (see Phase 4 table); sequence beats so the ad reads as product-led, not only ambiance
- Subtle over dramatic: Gentle camera movements maintain product focus; avoid a single monotonous move for the entire clip
- Duration constraint: Keep videos ≤10 seconds for social media optimization
- Resolution: 720p (960x960 or 1280x720) balances quality and file size
Audio Integration
- Check directory first: Use
ls *.mp3 to detect existing audio files
- Trim to video length: Use
-t 10 flag to match video duration
- Quality settings: AAC codec at 192kbps for good quality/size ratio
Error Handling
Common Issues & Solutions
| Issue | Solution |
|---|
| Multiple characters appear in composition | Add explicit constraint in prompt: "ONLY ONE [character], no other characters" |
| Plain white background | Specify environment details: "in a modern living room with wooden floor, beige walls, natural window light" |
| Image file too large | Compress before analysis using provided Python script |
| Audio sync issues | Ensure -shortest flag in FFmpeg to trim to shortest stream |
| Video generation timeout | Use background task spawning for long operations |
Generalization Notes
Adaptability Across Product Categories
This workflow is product-agnostic and can be applied to:
- Pet products: Use pet characters (cats, dogs, birds)
- Home goods: Use lifestyle characters or pure environment scenes
- Tech gadgets: Use user personas or hands-on demonstrations
- Fashion items: Use model characters in appropriate settings
- Food products: Use dining scenes or ingredient close-ups
Scalability Considerations
- Batch processing: Extend workflow to process multiple products in parallel
- Template system: Create environment templates for different product categories
- A/B testing: Generate multiple composition variants with different environments
- Localization: Adjust environment aesthetics for different cultural markets
Example Use Cases
Use Case 1: Pet Product (With Character Image)
Input: cat_tower.jpg, calico_cat.jpg
→ Compose: Cat on tower in cozy living room
→ Video: 10s with gentle camera pan + user's "Cat Republic.mp3"
Output: final_ad_video.mp4
Use Case 2: Furniture (No Character Image)
Input: modern_sofa.jpg
→ Generate: Lifestyle character reading on sofa
→ Compose: Character + sofa in bright apartment
→ Video: 10s with AI-generated ambient music
Output: final_ad_video.mp4
Use Case 3: Tech Gadget (No Character, No Audio)
Input: wireless_earbuds.jpg
→ Generate: Hands holding earbuds
→ Compose: Hands + earbuds on minimalist desk
→ Video: 10s with AI-generated tech music
Output: final_ad_video.mp4
Technical Requirements
Dependencies
- Python 3.8+ with PIL/Pillow for image processing
- FFmpeg for video/audio merging
- AI Services:
media_comprehension skill for image analysisimage_generator for composition and character generationvideo_diffusion for video creation
File Naming Conventions
- Product images:
product_*.jpg/png
- Character images:
character_*.jpg/png or descriptive names
- Audio files:
*.mp3
- Output composition:
composed_ad_image.png
- Final video:
final_ad_video.mp4 or [product_name]_ad.mp4
Conclusion
This workflow provides a systematic, generalizable approach to advertisement video creation that:
- ✅ Handles both complete and incomplete asset sets
- ✅ Ensures realistic environment integration
- ✅ Maintains character consistency (single character constraint)
- ✅ Flexibly manages audio from multiple sources
- ✅ Produces professional-quality output suitable for social media and e-commerce platforms
By following these guidelines, future users can efficiently create compelling advertisement videos for diverse product categories without overfitting to specific examples.
