wedding-immortalist

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Chinese

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.
将婚礼照片和视频转换为永恒的沉浸式3D体验,打造鲜活的记忆,让新人与宾客能永远重温这份美好。

When to Use This Skill

何时使用本技能

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
适用场景:
  • 将数千张婚礼照片处理为3DGS场景
  • 创建剧场模式体验,仪式/招待会的瞬间可在场景原位播放
  • 构建带最佳照片筛选功能的人脸聚类宾客名册
  • 匹配婚礼主题(迪斯科、乡村风、海滩风、现代风、酷儿庆典等)的设计美学
  • 基于AI美学评分,为每位宾客精选照片
不适用场景:
  • 普通照片编辑 → 请使用native-app-designer
  • 非婚礼场景的3DGS处理 → 请使用drone-inspection-specialist
  • 活动策划 → 本工具并非婚礼策划师
  • 无3D重建需求的视频编辑

Core Pipeline

核心流程

┌─────────────────────────────────────────────────────────────────┐
│                    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  │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│                    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

主题自适应设计

Theme Detection & Matching

主题检测与匹配

Every wedding has a unique aesthetic. Extract and honor it:
Theme TypeColor PaletteTypographyUI Elements
70s DiscoGold, orange, burnt sienna, deep purpleGroovy script, bold sansMirror balls, starbursts, warm gradients
Rustic/BarnEarth tones, sage, cream, woodSerif, hand-letteredBurlap textures, wildflower accents
Beach/CoastalOcean blues, sand, coral, seafoamLight sans, scriptShell motifs, wave patterns
Modern MinimalBlack, white, metallicsClean geometric sansSharp lines, negative space
Queer JoyRainbow spectrums, bold colorsExpressive, variedPride elements, celebration maximalism
Cultural FusionPer traditionTraditional + modernCultural motifs, heritage patterns
每场婚礼都有独特的美学风格,我们会提取并遵循这种风格:
主题类型配色方案字体风格UI元素
70s Disco金色、橙色、焦赭色、深紫色复古花体、粗体无衬线镜面球、星爆图案、暖色调渐变
Rustic/Barn(乡村/谷仓风)大地色系、鼠尾草绿、奶油白、木色衬线体、手写风格粗麻布纹理、野花装饰
Beach/Coastal(海滩/海滨风)海洋蓝、沙色、珊瑚色、海沫绿轻盈无衬线体、花体贝壳元素、波浪图案
Modern Minimal(现代极简风)黑、白、金属色简洁几何无衬线体锐利线条、留白设计
Queer Joy(酷儿庆典风)全彩虹色系、大胆亮色富有表现力的多元字体骄傲元素、极致庆典风格
Cultural Fusion(文化融合风)遵循传统配色传统+现代结合文化符号、传统纹样

Extracting Theme from Photos

从照片中提取主题

python
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python
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Theme extraction signals

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.' }
undefined
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.' }
undefined

3D Gaussian Splatting Pipeline

3D Gaussian Splatting流程

Photo/Video Ingestion

照片/视频导入

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
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

COLMAP运动结构恢复

bash
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bash
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Feature extraction

Feature extraction

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

Exhaustive matching for comprehensive coverage

Exhaustive matching for comprehensive coverage

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

Sparse reconstruction

Sparse reconstruction

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

Dense reconstruction (optional, for mesh)

Dense reconstruction (optional, for mesh)

colmap image_undistorter ... colmap patch_match_stereo ...
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colmap image_undistorter ... colmap patch_match_stereo ...
undefined

3DGS Training

3DGS训练

python
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python
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Wedding-optimized 3DGS settings

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, }
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

Multi-space merge strategy

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

Train each separately, then create unified navigation

Train each separately, then create unified navigation

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Face Clustering System

人脸聚类系统

Pipeline

流程

┌────────────────────────────────────────────────────────┐
│               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                   │
└────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────┐
│               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

聚类参数

python
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',
}
python
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 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. ]
undefined
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. ]
undefined

Identity Linking Workflow

身份关联工作流

  1. Couple identification: User tags couple in 2-3 photos
  2. Wedding party: User identifies key people
  3. Auto-propagation: Embeddings match across all photos
  4. Guest matching: Optional guest list import for name assignment
  5. Manual corrections: UI for fixing mismatches
  1. 新人识别:用户在2-3张照片中标记新人
  2. 婚礼团队识别:用户标记关键人员
  3. 自动传播:通过嵌入特征在所有照片中匹配身份
  4. 宾客匹配:可选导入宾客名单以分配姓名
  5. 手动修正:提供UI用于修正匹配错误

Aesthetic Scoring

美学评分

Per-Photo Quality Metrics

单张照片质量指标

python
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',
}
python
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

为每位宾客精选最佳照片

python
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)
python
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

剧场模式

Moment Detection & Playback

瞬间检测与回放

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
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

场景内视频投影

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
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

Web查看器架构

javascript
// 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',
  },
};
javascript
// 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

反模式

"All Frames, All the Time"

"全帧全量处理"

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.
错误做法:提取视频的每一帧用于3DGS处理。 原因:数据冗余,处理速度慢10倍,且无质量提升。 正确做法:以2-3帧/秒的频率提取,并基于运动选择关键帧。

"One Giant Scene"

"单一巨型场景"

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.
错误做法:为整个场地训练单个3DGS模型。 原因:内存占用爆炸,质量下降,消费级硬件无法运行。 正确做法:按区域分别训练,创建带无缝过渡的统一导航系统。

"Default Clustering Threshold"

"默认聚类阈值"

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.
错误做法:使用HDBSCAN的默认设置。 原因:婚礼照片的光线、妆容、角度差异大,需要针对性调整。 正确做法:根据照片数量和质量差异为每场婚礼调整参数。

"Ignoring Theme"

"忽略主题风格"

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.
错误做法:为迪斯科主题婚礼使用通用的白/灰色查看器UI。 原因:破坏了活动的个性与欢乐氛围。 正确做法:提取并遵循新人的美学选择。

"Photographer Only"

"仅使用专业照片"

Wrong: Using only professional photos. Why: Misses candid moments, guest perspectives, coverage gaps. Right: Merge professional + guest photos for complete coverage.
错误做法:只使用专业摄影师拍摄的照片。 原因:错过 candid 瞬间、宾客视角和拍摄盲区。 正确做法:合并专业照片与宾客上传的照片以获得完整覆盖。

Guest Experience Features

宾客体验功能

Shareable Guest Galleries

可分享的宾客画廊

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
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

协作增强

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

输出交付物

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/
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

集成点

  • 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.
  • drone-inspection-specialist:3DGS技术、COLMAP流程
  • collage-layout-expert:照片排列、美学构图
  • color-theory-palette-harmony-expert:主题色彩提取
  • clip-aware-embeddings:照片-文本匹配用于搜索
  • photo-composition-critic:美学质量评分

核心理念:婚礼一生只有一次,记忆却应永存。本技能将短暂的瞬间转化为永恒的可探索体验,致敬新人独特的庆典——无论是迪斯科舞会、乡村谷仓聚会,还是两位新郎与chosen family共同庆祝的爱情时刻。