Transform Video into Storyboards with AI
How we built an advanced pipeline that turns hours of footage into structured, searchable storyboards in minutes.
Curify AI Team
AI Research Team
Imagine being able to upload hours of raw footage and within minutes get a detailed, scene-by-scene breakdown of your entire video. That's exactly what our AI-powered scene detection system delivers.
Built with cutting-edge Python libraries and deep learning models, this pipeline doesn't just detect scene changes—it understands the content, identifies key elements, and structures everything into a comprehensive storyboard.
The scene detection pipeline in action, identifying key moments and generating structured storyboards
Pro Tip
How It Works: Under the Hood
Video Processing Pipeline
Our system processes videos through a sophisticated multi-stage pipeline that ensures accurate scene detection and analysis:
Seamless Video Integration
Process local files, YouTube links, or cloud storage with our unified interface.
Customizable Output
Export metadata to JSON format for integration with other tools.
Camera Motion Detection
Automatically identify pans, zooms, and other camera movements.
AI-Powered Analysis
Enhance scene understanding with our optional AI analysis module.
Powerful Features at Your Fingertips
Seamless Video Integration
Process local files, YouTube links, or cloud storage with our unified interface.
Seamless Video Integration
Process local files, YouTube links, or cloud storage with our unified interface.
Camera Motion Detection
Automatically identify pans, zooms, and other camera movements.
Customizable Output
Export metadata to JSON format for integration with other tools.
Performance Optimized
Rich, Structured Output
Our system generates comprehensive storyboard data with detailed metadata for each scene, giving you complete control over your video content.
{ "scenes": [ { 1"scene_id": 1, 0.0"start_time": 0.0, 5.2"end_time": 5.2, "key_frame": "path/to/keyframe.jpg", "shot_type": "establishing", "camera_move": "static", "detected_objects": ["person", "car", "building"] } ], "metadata": { 120.5"duration": 120.5, "resolution": "1920x1080", 30"fps": 30 }}Export Option
- Export Option
- Export Option
- Export Option
- Export Option
Export Option
Easy Integration
The structured JSON output makes it easy to integrate with other tools and workflows::
The Power of AI-Powered Scene Analysis
- Modular Architecture - The system is built with separate components for video analysis, AI processing, and output generation, making it easy to extend and maintain.
- Performance Optimized - Efficient frame processing and parallelization ensure fast analysis even for long videos.
- AI-Enhanced Analysis - Optional AI components provide deeper scene understanding and more accurate labeling.
Advanced Usage & Customization
The scene detection system is highly customizable to fit different use cases. Here are some advanced features and customization options:
Custom Scene Detection Thresholds
Adjust the sensitivity of scene detection by modifying the threshold parameter. Lower values make the detection more sensitive to changes.
AI-Enhanced Analysis
Enable AI analysis for more detailed scene understanding and labeling. This requires additional setup with the Ollama server.
Output Customization
Customize the output format and include additional metadata in the generated storyboard.
Integration with Other Tools
The storyboard output can be easily integrated with other tools and workflows. Here are some examples:
- 1Video Editing Software - Import the JSON output into video editors that support script-based editing
- 2Content Management Systems - Automatically generate metadata for video assets
- 3AI Training Data - Use the structured output as training data for machine learning models
Dream Level Analysis: Inception Scene Breakdown
Explore how our AI analyzes the complex dream layers and visual effects in Inception:
Analysis: Dream layer detection and visual effect breakdown
Scene Analysis Breakdown
Scene 1 (1.50s)
A woman stands on a sidewalk, looking to the side. A man stands behind her.
Real-World Example: Titanic Scene Analysis
Watch how our system analyzes a scene from Titanic, detecting shot changes and generating detailed scene metadata:
Analysis: Scene detection and metadata extraction in real-time
Understanding Scene Detection Output
Let's break down a typical scene detection output to understand how our AI analyzes and structures video content. Below each explanation, you'll find the corresponding JSON structure that powers these insights.
1. Scene Identification
Each scene is assigned a unique identifier and timestamp range, allowing for precise navigation through the video content. This forms the foundation of our analysis.
JSON Structure:
{
"scene_id": "scene_001",
"start_time": 2.5,
"end_time": 5.2,
"duration": 2.7,
"keyframe_index": 5,
"keyframe_time": 3.8
}This JSON structure shows the basic identification data for a scene, including its unique ID, timing information, and the index/time of its representative keyframe.
2. Visual Analysis
Our AI examines keyframes to understand the visual composition of each scene, including dominant colors, lighting conditions, and visual elements.
JSON Structure:
{
"visual_analysis": {
"brightness": 0.78,
"contrast": 0.65,
"color_palette": [
"#3A5FCD",
"#87CEEB",
"#F5F5DC"
],
"dominant_colors": [
{
"color": "#3A5FCD",
"percentage": 0.45
},
{
"color": "#87CEEB",
"percentage": 0.35
},
{
"color": "#F5F5DC",
"percentage": 0.2
}
],
"lighting_condition": "daylight",
"environment": "outdoor",
"detected_objects": [
{
"label": "person",
"confidence": 0.97,
"count": 2
},
{
"label": "sky",
"confidence": 0.99,
"count": 1
}
]
}
}This JSON shows the visual analysis data, including color information, lighting conditions, and detected objects with confidence scores.
3. Shot Composition
Within each scene, we identify individual shots and their transitions, helping understand the visual flow and pacing of the content.
JSON Structure:
{
"shots": [
{
"shot_id": "shot_001",
"start_time": 2.5,
"end_time": 3.1,
"transition": {
"type": "cut",
"confidence": 0.98
},
"camera_motion": {
"type": "static",
"confidence": 0.92
}
},
{
"shot_id": "shot_002",
"start_time": 3.1,
"end_time": 4.3,
"transition": {
"type": "fade",
"duration": 0.3,
"confidence": 0.95
},
"camera_motion": {
"type": "pan_left",
"confidence": 0.88
}
}
]
}This JSON structure details the shot composition within a scene, including timing, transition types, and camera motion analysis.
4. Content Classification
Scenes are automatically categorized based on their content, making it easy to find specific types of footage later.
JSON Structure:
{
"content_analysis": {
"primary_category": "drama",
"secondary_categories": [
"romance",
"disaster"
],
"setting": {
"type": "ship_deck",
"time_of_day": "night",
"confidence": 0.92
},
"subjects": [
{
"type": "main_character",
"name": "Jack",
"position": "center_frame",
"emotion": "determined",
"confidence": 0.89
},
{
"type": "main_character",
"name": "Rose",
"position": "center_frame",
"emotion": "fearful",
"confidence": 0.91
}
],
"sentiment": {
"overall": "intense_dramatic",
"confidence": 0.88,
"emotions": [
"fear",
"determination",
"urgency"
]
},
"key_elements": [
"lifeboat",
"ocean",
"moonlight"
],
"narrative_importance": 0.95,
"action_required": true
}
}This JSON shows how the AI analyzes and classifies movie scenes, including character emotions, setting details, and narrative importance, with Titanic's dramatic lifeboat scene as an example.
Putting It All Together
By combining these elements, our system creates a comprehensive map of your video content. This structured data powers features like intelligent search, automated editing, and content analysis.
Complete Scene Data Example
Here's how all the pieces come together in a complete scene analysis:
{
"scene_id": "scene_001",
"start_time": 2.5,
"end_time": 5.2,
"duration": 2.7,
"metadata": {
"created_at": "2025-12-11T14:25:30Z",
"video_source": "interview_001.mp4",
"resolution": "1920x1080",
"fps": 30
},
"visual_analysis": {
"brightness": 0.78,
"contrast": 0.65,
"color_palette": [
"#3A5FCD",
"#87CEEB",
"#F5F5DC"
],
"lighting_condition": "daylight",
"environment": "studio"
},
"audio_analysis": {
"has_speech": true,
"speech_confidence": 0.92,
"background_noise_level": 0.15,
"speaker_gender": [
"male",
"female"
],
"speech_text": "Let's discuss how AI is transforming video production..."
},
"content_analysis": {
"primary_category": "interview",
"setting": "studio",
"subjects": [
"host",
"guest"
],
"sentiment": "neutral_positive"
},
"shots": [
{
"shot_id": "shot_001",
"start_time": 2.5,
"end_time": 3.1,
"keyframe": "https://example.com/keyframes/scene_001_shot_001.jpg",
"transition": {
"type": "cut",
"confidence": 0.98
}
},
{
"shot_id": "shot_002",
"start_time": 3.1,
"end_time": 5.2,
"keyframe": "https://example.com/keyframes/scene_001_shot_002.jpg",
"transition": {
"type": "fade",
"confidence": 0.95
}
}
]
}Key Benefits
- Efficient Editing: Jump directly to any scene or shot without scrubbing through hours of footage
- Smart Search: Find content based on visual elements, not just metadata
- Consistent Quality: Identify and maintain visual consistency across your project
- Data-Driven Decisions: Get insights into your content structure and pacing
Transforming Video Production with AI
AI-powered scene detection is revolutionizing how we approach video production. By automating the tedious process of scene identification and organization, creators can focus on what truly matters – telling compelling stories. Our technology bridges the gap between raw footage and polished content, making professional-grade video analysis accessible to everyone.
As we continue to refine our algorithms and expand our capabilities, we're excited to see how filmmakers, educators, and content creators will leverage these tools to push the boundaries of visual storytelling. The future of video production is here, and it's more efficient and creative than ever before.