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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 23:52:15 +09:00

5.1 KiB

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack
id title category status canonical_id aliases duplicate_of source_trust_level confidence_score verification_status tags raw_sources last_reinforced github_commit tech_stack
wiki-2026-0508-lighting-composition Lighting & Composition 10_Wiki/Topics verified self
Cinematography
Visual Composition
Lighting Design
none A 0.9 applied
photography
cinematography
generative-AI
prompt-engineering
2026-05-10 pending
language framework
prompt FLUX-Sora-Veo

Lighting & Composition

매 한 줄

"매 image / video 의 emotional weight 의 90%는 lighting + composition". 매 subject 무엇이든, 매 light direction / quality, 매 frame organization 가 매 narrative 를 carry. 매 2026 의 generative AI (FLUX 1.1, Sora 2, Veo 3) 도 매 same vocabulary 를 prompt 로 받아 매 cinematographic control 가능.

매 핵심

매 lighting 의 axes

  • Direction: front / side / back / top / bottom (각 emotional valence 다름).
  • Quality: hard (sharp shadows) vs soft (diffused).
  • Color temperature: warm (3000K) vs cool (6500K).
  • Ratio: key:fill ratio (1:1 flat, 4:1 dramatic).

매 composition 의 axes

  • Rule of thirds: 매 subject 매 third lines 의 intersection.
  • Leading lines: 매 viewer's eye 의 directing.
  • Negative space: 매 emptiness 가 carry meaning.
  • Depth layers: foreground / mid / background.
  • Aspect ratio: 16:9 (cinematic), 9:16 (mobile), 1:1 (square), 2.39:1 (anamorphic).

매 응용

  1. Photography: portrait / product / landscape lighting setups.
  2. Cinematography: 매 scene mood 의 establishing.
  3. Generative AI prompts: 매 FLUX/Sora/Veo 매 cinematographic prompt vocabulary 인식.
  4. UI design: 매 hero image direction selection.

💻 패턴

Three-point lighting prompt

A portrait of a software engineer, three-point lighting: 
key light from camera-left at 45 degrees (soft, 5500K), 
fill light from camera-right at 1:2 ratio, 
backlight rim from upper-right (warm 3200K), 
shallow depth of field, 85mm lens equivalent, 
shot on Arri Alexa 35.

Rembrandt lighting (FLUX 1.1 prompt)

flux_prompt = """
Rembrandt lighting on subject's face: 
small triangle of light on shadow-side cheek, 
key light camera-left high at 45°, 
deep shadow on right side, 
chiaroscuro mood, oil painting aesthetic, 
photorealistic, 8K detail.
"""

Golden hour video (Sora 2)

sora_prompt = {
    "shot": "tracking shot through wheat field",
    "lighting": "golden hour, sun low at 15° angle camera-back-left, "
                "warm 2800K, long shadows, lens flare",
    "composition": "rule of thirds, horizon on lower third, "
                   "leading lines from wheat rows toward subject",
    "camera": "Steadicam, 24fps, 35mm anamorphic, T2.0",
    "duration_s": 8,
}

Composition checker (CV-based QA)

import cv2
import numpy as np

def rule_of_thirds_score(img: np.ndarray) -> float:
    """매 saliency map peak 매 third-line proximity."""
    h, w = img.shape[:2]
    saliency = cv2.saliency.StaticSaliencyFineGrained_create()
    _, sal = saliency.computeSaliency(img)
    peak_y, peak_x = np.unravel_index(sal.argmax(), sal.shape)
    third_lines_x = [w/3, 2*w/3]
    third_lines_y = [h/3, 2*h/3]
    dx = min(abs(peak_x - tx) for tx in third_lines_x) / w
    dy = min(abs(peak_y - ty) for ty in third_lines_y) / h
    return 1.0 - min(dx, dy) * 2  # higher = better composition

Color temp grading (Veo 3 prompt augmentation)

def grade_prompt(base: str, mood: str) -> str:
    grades = {
        "warm_nostalgic": "teal-orange grade, warm midtones (3200K), cool shadows",
        "cold_clinical": "desaturated blues, 6500K key, high-key flat lighting",
        "noir": "high-contrast B&W, low-key, single hard source, 4:1 ratio",
    }
    return f"{base} | grade: {grades[mood]}"

매 결정 기준

Mood Lighting Composition
Heroic Backlight rim + low-key fill Low angle, centered
Intimate Soft key, high ratio Close-up, off-center
Tense Hard side light, deep shadow Dutch tilt, asymmetric
Whimsical Bright fill, warm tones Wide, symmetrical
Documentary Available light Eye-level, rule of thirds

기본값: 매 three-point + rule of thirds — 매 safe baseline.

🔗 Graph

🤖 LLM 활용

언제: 매 image/video prompt engineering — 매 cinematographic vocabulary 매 quality lift 큼. 언제 X: 매 abstract / non-representational generation — vocabulary 의 X.

안티패턴

  • Flat front lighting: 매 amateur look — depth loss.
  • Centered everything: 매 visual boring.
  • Mixed color temps unintentional: 매 amateur giveaway.
  • Over-prompting: 매 50+ tokens 의 lighting → 매 model confusion.

🧪 검증 / 중복

  • Verified (Brown Cinematography; Block Visual Story; FLUX 1.1 prompt guide; Sora 2 system card 2025).
  • 신뢰도 A.

🕓 Changelog

날짜 변경
2026-05-08 Phase 1
2026-05-10 Manual cleanup — lighting/composition vocabulary + 2026 generative AI prompts