ad-video-create

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:

1. Asset Discovery: Scan working directory for available assets
2. 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:

1. 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
2. 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:

1. Prepare input images:
   • Product image (original or compressed if >50KB)
   • Character image (from Phase 2 or user-provided)
2. 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):

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)

1. 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"
     }
   }
   

2. 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)

1. 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

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 analysis
  • image_generator for composition and character generation
  • video_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.