Wedding Immortalist

Wedding Immortalist

Transform wedding photos and video into an eternal, immersive 3D experience. Create living memories that let couples and guests relive the magic forever.

DECISION POINTS

1. Multi-Space Merge Strategy

Input: Multiple venue spaces detected (ceremony, reception, cocktail hour)
├── < 500 photos per space
│   └── Train unified scene (risk: quality loss, benefit: seamless navigation)
├── 500-1500 photos per space  
│   └── Train per-space with portal transitions (RECOMMENDED)
├── > 1500 photos per space
│   └── Split large spaces into sub-regions to avoid memory overflow
└── Mixed lighting conditions?
    ├── Yes → Separate indoor/outdoor scenes
    └── No → Can merge with lighting adaptation

2. Face Clustering Threshold Selection

Input: Face embedding similarity scores across all detected faces
├── Photo count < 1000
│   └── Conservative threshold (0.4): Prefer precision over recall
├── Photo count 1000-5000
│   └── Balanced threshold (0.3): Standard wedding size
├── Photo count > 5000
│   └── Aggressive threshold (0.25): Large weddings need looser matching
└── Professional + phone photos mixed?
    ├── Yes → Increase threshold by 0.05 (lighting/quality variance)
    └── No → Use base threshold

3. Theme Signal Conflict Resolution

Input: Competing aesthetic signals detected
├── Venue theme ≠ Decor theme
│   ├── Venue rustic, decor modern → Weight decor 70%, venue 30%
│   └── Venue formal, decor casual → Weight venue 60%, decor 40%
├── Multiple cultural traditions
│   └── Create fusion palette prioritizing couple's primary heritage
├── Seasonal vs. chosen colors
│   ├── Spring wedding with fall colors → Honor chosen colors
│   └── Christmas wedding ignoring season → Blend seasonal + chosen
└── Guest attire contradicts theme
    └── Ignore guest attire, focus on venue + intentional decor

4. Video Frame Extraction Rate

Input: Wedding video files with metadata
├── Handheld/phone video (high motion blur)
│   └── Extract 1 fps to reduce blur frames
├── Professional video (smooth)
│   └── Extract 2-3 fps for optimal overlap
├── Drone footage
│   └── Extract 1 fps (smooth motion, redundant frames)
├── Ceremony (mostly static)
│   └── Extract 1 fps, boost for processional/recessional
└── Reception/dancing (high motion)
    └── Extract 3 fps to capture movement variety

5. Space Skip Threshold Decision

Input: Reconstructed space quality metrics
├── < 50 images AND reconstruction error > 5.0 pixels
│   └── Skip space: insufficient coverage, warn user
├── 50-100 images AND error > 3.0 pixels
│   └── Include with quality warning, mark as "preview quality"
├── > 100 images AND error > 2.0 pixels
│   └── Retrain with adjusted parameters, likely lighting issues
└── Blurry photos > 40% of space images
    └── Skip space, request better photos from user

FAILURE MODES

1. COLMAP Divergence Cascade

Symptom: Sparse reconstruction fails completely, no 3D points generated Detection Rule: If COLMAP outputs < 100 3D points for > 200 input images Diagnosis: Feature matching failure due to repetitive textures (white walls, flowers) or extreme lighting changes Fix:

• Re-extract features with lower max_image_size (1600px)
• Switch to sequential matcher instead of exhaustive
• Filter images by Laplacian variance > 150 to remove blur

2. Face-Cluster False Merge Explosion

Symptom: Single cluster contains 3+ distinct people, especially bridesmaids in similar makeup Detection Rule: If cluster size > 30 photos AND intra-cluster cosine distance variance > 0.15 Diagnosis: Similar makeup, lighting, or formal poses confusing embeddings Fix:

• Reduce clustering threshold by 0.05
• Apply makeup-invariant embedding model (AdaFace instead of ArcFace)
• Manual split using user confirmation of cluster samples

3. Theme Signal Chaos

Symptom: UI colors clash horribly, typography doesn't match aesthetic Detection Rule: If extracted color palette has > 8 dominant colors OR no single color > 15% dominance Diagnosis: Too many competing signals from mixed lighting, decorations, clothing Fix:

• Focus extraction on venue + decor photos only, ignore people
• Apply k-means clustering to consolidate palette to 4-5 colors
• Fallback to safe neutral palette with couple's stated preferences

4. Memory Overflow Death Spiral

Symptom: 3DGS training crashes, viewer freezes on load Detection Rule: If trained .ply file > 2GB OR browser memory usage > 8GB Diagnosis: Too many Gaussian points from over-densification or merged scenes Fix:

• Split scene into smaller spatial regions
• Reduce densify_grad_threshold from 0.0002 to 0.0005
• Implement LOD (level-of-detail) rendering with quality presets

5. Guest Photo Desert

Symptom: Many face clusters have only 1-2 poor-quality photos Detection Rule: If > 30% of clusters have < 3 photos OR average aesthetic score < 0.4 Diagnosis: Insufficient photo coverage or professional photographer focused only on couple Fix:

• Request guest phone photos to fill gaps
• Lower aesthetic threshold for rare-appearance guests
• Generate "best available" galleries with quality disclaimers

WORKED EXAMPLES

Example 1: Large Disco Wedding (500 guests, 3000 photos)

Scenario: 1970s theme revival, disco balls, gold/orange/purple decor, mixed lighting (strobes + warm), professional + 50 guest phones

Step-by-step walkthrough:

1. Initial Assessment: 3000 photos, detect 4 spaces (ceremony, cocktail, reception, photo booth)

   • Decision Point 1 triggered: > 500 photos per space → train separately
   • Space distribution: ceremony (800), cocktail (400), reception (1500), photo booth (300)

2. Theme Extraction:

   • Color analysis finds: gold (22%), burnt orange (18%), deep purple (15%), brown (12%)
   • Era signals: disco ball reflections, wide lapels in photos, platform shoes
   • Typography match: groovy script + bold sans serif
   • Decision Point 3: Theme signals align → proceed with disco aesthetic

3. Face Clustering:

   • 2847 faces detected across all photos
   • Decision Point 2: Large wedding + mixed photo sources → threshold 0.25 + 0.05 = 0.3
   • Result: 287 unique identities (realistic for 500-guest wedding)

4. 3DGS Training:

   • Decision Point 4: Professional video → extract at 2 fps
   • Ceremony space training completes successfully
   • Reception space hits Decision Point 5: > 1500 images triggers sub-region split
   • Split reception into: dance floor, dining area, bar area

5. Quality Gates Check:

   • ✓ All spaces reconstructed with < 2.0 pixel error
   • ✓ Face clusters manually verified for couple + wedding party
   • ✓ Theatre mode markers placed at 12 key moments
   • ✓ UI theme matches gold/orange/purple palette
   • ✓ Guest galleries generated for 287 identities

Expert catches vs. Novice misses:

• Expert: Notices strobing lights will confuse COLMAP, filters those frames before processing
• Novice: Includes all frames, gets reconstruction failure, doesn't understand why
• Expert: Recognizes disco theme needs bold, groovy typography—not elegant script
• Novice: Applies generic wedding fonts, loses the personality

Example 2: Intimate Rustic Ceremony (50 guests, 400 photos)

Scenario: Barn venue, earth tones, string lights, sage/cream decor, single professional photographer

Key decision differences:

• Small photo count triggers Decision Point 1: unified scene approach
• Conservative face clustering (0.4 threshold) due to limited photos per person
• Earth-tone palette extraction straightforward (no conflicts)
• Single lighting condition simplifies reconstruction

Trade-off analysis:

• Benefit: Seamless navigation, no portal breaks
• Risk: Lower overall quality due to unified training
• Mitigation: Accept risk for small venue—user experience more important

QUALITY GATES

• All venue spaces reconstructed with COLMAP error < 3.0 pixels
• Face clustering achieves > 95% precision on couple + wedding party (verified manually)
• Theatre mode includes minimum 8 key moments with video/audio sync
• Color palette extraction yields cohesive 4-6 colors matching wedding aesthetic
• Best photo selection provides minimum 3 high-quality images per guest (where possible)
• Web viewer loads all scenes within 15 seconds on standard connection
• Guest roster correctly identifies minimum 90% of wedding party members
• UI theme visually matches extracted wedding aesthetic (validated by user)
• 3DGS file sizes total < 5GB for reasonable hosting/sharing
• All key moments playback smoothly without audio desync > 100ms

NOT-FOR BOUNDARIES

This skill should NOT be used for:

• General photo editing → Use native-app-designer instead for basic editing, filters, adjustments
• Non-wedding 3DGS projects → Use drone-inspection-specialist for architectural/industrial 3D scanning
• Wedding planning or vendor coordination → This is not a wedding planner, only processes existing photos/video
• Real-time event streaming → Use dedicated streaming solutions for live broadcast
• Professional photography instruction → Use photography education skills for technique training
• Video editing without 3D reconstruction → Use standard video editing skills for traditional editing needs

Delegate to other skills when:

• User wants basic photo slideshow → collage-layout-expert
• Need color palette advice before wedding → color-theory-palette-harmony-expert
• Want to improve individual photos → photo-composition-critic
• Need facial recognition for security → face-detection-specialist