wedding-immortalist

仓库: erichowens/some_claude_skills
安装量: 39
排名: #18256

安装

npx skills add https://github.com/erichowens/some_claude_skills --skill 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.

When to Use This Skill

Use for:

Processing thousands of wedding photos into 3DGS scenes Creating theatre-mode experiences where ceremony/reception moments play in-place Building face-clustered guest rosters with best-photo selection Matching design aesthetics to wedding themes (disco, rustic, beach, modern, queer celebrations) AI-curated photo selection per guest with aesthetic scoring

NOT for:

General photo editing → use native-app-designer Non-wedding 3DGS → use drone-inspection-specialist Event planning → not a wedding planner Video editing without 3D reconstruction Core Pipeline ┌─────────────────────────────────────────────────────────────────┐ │ WEDDING IMMORTALIST PIPELINE │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ 1. INGEST 2. RECONSTRUCT 3. CLUSTER │ │ ├─ Photos (1000s) ├─ COLMAP SfM ├─ Face detect │ │ ├─ Video (hours) ├─ 3DGS training ├─ Embeddings │ │ └─ Audio/speeches └─ Scene merge └─ Identity link │ │ │ │ 4. CURATE 5. DESIGN 6. PRESENT │ │ ├─ Aesthetic score ├─ Theme extract ├─ Web viewer │ │ ├─ Per-person best ├─ Color palette ├─ Theatre mode │ │ └─ Moment detect └─ Typography └─ Guest roster │ │ │ └─────────────────────────────────────────────────────────────────┘

Theme-Adaptive Design Theme Detection & Matching

Every wedding has a unique aesthetic. Extract and honor it:

Theme Type Color Palette Typography UI Elements 70s Disco Gold, orange, burnt sienna, deep purple Groovy script, bold sans Mirror balls, starbursts, warm gradients Rustic/Barn Earth tones, sage, cream, wood Serif, hand-lettered Burlap textures, wildflower accents Beach/Coastal Ocean blues, sand, coral, seafoam Light sans, script Shell motifs, wave patterns Modern Minimal Black, white, metallics Clean geometric sans Sharp lines, negative space Queer Joy Rainbow spectrums, bold colors Expressive, varied Pride elements, celebration maximalism Cultural Fusion Per tradition Traditional + modern Cultural motifs, heritage patterns Extracting Theme from Photos

Theme extraction signals

THEME_SIGNALS = { 'color_palette': 'Dominant colors from venue, florals, attire', 'lighting_mood': 'Warm/cool, natural/dramatic, string lights/chandeliers', 'decor_elements': 'Rustic/modern/vintage/eclectic', 'attire_style': 'Traditional/non-traditional, formal/casual', 'cultural_markers': 'Religious symbols, cultural traditions', 'era_aesthetic': '70s disco, 20s gatsby, etc.' }

3D Gaussian Splatting Pipeline Photo/Video Ingestion Optimal Input Strategy: ├── Video: Extract 2-3 fps (80% overlap minimum) ├── Photos: Include ALL photographer shots ├── Phone photos: Guest uploads (georeferenced bonus) └── Coverage: Ceremony + reception + all spaces

Quality Thresholds: ├── Minimum images per space: 50-100 ├── Overlap requirement: 60-80% ├── Blur rejection: Laplacian variance < 100 = skip └── Exposure: Reject severe over/underexposure

COLMAP Structure from Motion

Feature extraction

colmap feature_extractor \ --database_path database.db \ --image_path images/ \ --ImageReader.single_camera 0 \ --SiftExtraction.max_image_size 3200

Exhaustive matching for comprehensive coverage

colmap exhaustive_matcher \ --database_path database.db \ --SiftMatching.guided_matching 1

Sparse reconstruction

colmap mapper \ --database_path database.db \ --image_path images/ \ --output_path sparse/

Dense reconstruction (optional, for mesh)

colmap image_undistorter ... colmap patch_match_stereo ...

3DGS Training

Wedding-optimized 3DGS settings

WEDDING_3DGS_CONFIG = { 'iterations': 50_000, # High quality for permanent archive 'densify_from_iter': 500, 'densify_until_iter': 15_000, 'densification_interval': 100, 'opacity_reset_interval': 3000, 'sh_degree': 3, # Full spherical harmonics for lighting 'percent_dense': 0.01, 'densify_grad_threshold': 0.0002, }

Multi-space merge strategy

SPACES = ['ceremony', 'cocktail_hour', 'reception', 'photo_booth', 'dance_floor']

Train each separately, then create unified navigation

Face Clustering System Pipeline ┌────────────────────────────────────────────────────────┐ │ FACE CLUSTERING PIPELINE │ ├────────────────────────────────────────────────────────┤ │ 1. Detection (RetinaFace/MTCNN) │ │ └─ All faces in all photos │ │ 2. Alignment (5-point landmark) │ │ └─ Standardize for embedding │ │ 3. Embedding (ArcFace/AdaFace) │ │ └─ 512-dim identity vector per face │ │ 4. Clustering (HDBSCAN) │ │ └─ Group by identity, handle edge cases │ │ 5. Identity Linking │ │ └─ Match to couple, wedding party, family, guests │ │ 6. Best Photo Selection │ │ └─ Aesthetic scoring per cluster │ └────────────────────────────────────────────────────────┘

Clustering Parameters CLUSTERING_CONFIG = { 'min_cluster_size': 3, # At least 3 photos to form identity 'min_samples': 2, 'metric': 'cosine', 'cluster_selection_epsilon': 0.3, 'cluster_selection_method': 'eom', }

Identity priority for naming

IDENTITY_PRIORITY = [ 'couple_1', 'couple_2', # The married couple 'wedding_party', # Bridesmaids, groomspeople 'parents', # Parents of the couple 'grandparents', 'siblings', 'extended_family', 'friends', 'vendors', # Photographer, DJ, etc. ]

Identity Linking Workflow Couple identification: User tags couple in 2-3 photos Wedding party: User identifies key people Auto-propagation: Embeddings match across all photos Guest matching: Optional guest list import for name assignment Manual corrections: UI for fixing mismatches Aesthetic Scoring Per-Photo Quality Metrics AESTHETIC_FEATURES = { # Technical quality 'sharpness': 'Laplacian variance, MTF analysis', 'exposure': 'Histogram analysis, dynamic range', 'noise': 'High-ISO detection, grain analysis',

# Composition
'rule_of_thirds': 'Subject placement scoring',
'symmetry': 'For venue/group shots',
'framing': 'Negative space, balance',

# Face-specific
'expression': 'Smile detection, eye openness',
'blink_detection': 'Eyes closed penalty',
'gaze_direction': 'Looking at camera vs. candid',
'face_occlusion': 'Nothing blocking the face',
'face_lighting': 'Even illumination, no harsh shadows',

# Emotional
'genuine_smile': 'Duchenne marker detection',
'moment_quality': 'Laughter, tears, embraces',

}

Best Photo Selection Per Person def select_best_photos(cluster_photos, n=5): """Select top N photos for a person across all their appearances."""

scores = []
for photo in cluster_photos:
    score = (
        0.25 * technical_quality(photo) +
        0.25 * composition_score(photo) +
        0.30 * expression_quality(photo) +
        0.20 * context_diversity(photo, scores)  # Avoid all similar shots
    )
    scores.append((photo, score))

# Select top N with diversity constraint
return diverse_top_n(scores, n, diversity_threshold=0.7)

Theatre Mode Moment Detection & Playback KEY MOMENTS (auto-detected + user-tagged): ├── Ceremony │ ├── Processional │ ├── Vows exchange │ ├── Ring ceremony │ ├── First kiss │ └── Recessional ├── Reception │ ├── Grand entrance │ ├── First dance │ ├── Parent dances │ ├── Toasts/speeches │ ├── Cake cutting │ └── Bouquet/garter ├── Party │ ├── Dance floor highlights │ └── Exit/sendoff └── Candids ├── Emotional moments (tears, laughter) └── Spontaneous joy

In-Scene Video Projection Theatre Mode Rendering: 1. User navigates 3DGS scene freely 2. Approaches "moment marker" (glowing orb/frame) 3. Video/slideshow plays IN the 3D space ├── On walls where projector was ├── Floating frames in dance floor area └── Photo booth backdrop location 4. Spatial audio for speeches/music 5. User can pause, scrub, exit to continue exploring

Web Viewer Architecture // Wedding Immortalist Viewer Components const VIEWER_FEATURES = { // 3DGS Navigation gaussianSplatting: { renderer: 'three-gaussian-splat', navigation: 'orbit + first-person', qualityLevels: ['preview', 'standard', 'maximum'], },

// Theatre Mode theatreMode: { momentMarkers: true, videoInScene: true, spatialAudio: true, transitionEffects: 'theme-matched', },

// Guest Roster guestRoster: { faceGrid: 'clustered by identity', photoGallery: 'per-person best shots', searchByName: true, shareableLinks: 'per-guest galleries', },

// Theme theming: { colorPalette: 'extracted from wedding', typography: 'theme-matched', uiElements: 'aesthetic-consistent', }, };

Anti-Patterns "All Frames, All the Time"

Wrong: Extracting every video frame for 3DGS. Why: Redundant data, 10x slower processing, no quality improvement. Right: 2-3 fps extraction with motion-based keyframe selection.

"One Giant Scene"

Wrong: Training single 3DGS for entire venue. Why: Memory explosion, quality degradation, impossible on consumer hardware. Right: Train per-space, create unified navigation with seamless transitions.

"Default Clustering Threshold"

Wrong: Using default HDBSCAN settings. Why: Wedding photos have varying lighting, makeup, angles—need tuning. Right: Tune per-wedding based on photo count and quality variance.

"Ignoring Theme"

Wrong: Generic white/gray viewer UI for disco wedding. Why: Destroys the personality and joy of the event. Right: Extract and honor the couple's aesthetic choices.

"Photographer Only"

Wrong: Using only professional photos. Why: Misses candid moments, guest perspectives, coverage gaps. Right: Merge professional + guest photos for complete coverage.

Guest Experience Features Shareable Guest Galleries Per-Guest Experience: ├── Personalized link: yourwedding.com/guests/aunt-martha ├── Their best photos (AI-curated) ├── Photos with the couple ├── Group photos they appear in ├── Download options (full-res) └── "Add to my memories" for their own archives

Collaborative Enhancement Guest Contribution Portal: ├── Upload their own photos ├── Tag themselves in unidentified clusters ├── Correct misidentifications ├── Add names to unknown guests └── Submit video moments they captured

Output Deliverables wedding-immortalist-output/ ├── 3dgs-scenes/ │ ├── ceremony/ │ ├── cocktail/ │ ├── reception/ │ └── unified-navigation.json ├── guest-roster/ │ ├── face-clusters/ │ ├── identity-mapping.json │ └── per-person-galleries/ ├── theatre-mode/ │ ├── moment-markers.json │ ├── video-segments/ │ └── spatial-audio/ ├── web-viewer/ │ ├── index.html │ ├── theme-config.json │ └── assets/ └── exports/ ├── full-resolution-photos/ ├── guest-gallery-zips/ └── video-compilations/

Integration Points drone-inspection-specialist: 3DGS techniques, COLMAP pipeline collage-layout-expert: Photo arrangement, aesthetic composition color-theory-palette-harmony-expert: Theme color extraction clip-aware-embeddings: Photo-text matching for search photo-composition-critic: Aesthetic quality scoring

Core Philosophy: A wedding happens once. The memories should live forever. This skill transforms ephemeral moments into an eternal, explorable experience that honors the couple's unique celebration—whether it's a disco dance party, a rustic barn gathering, or two grooms celebrating their love with chosen family.

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