Dunhuang Dance 'Flying to Heaven' Action Breakdown Poster AI Image Prompt
Classical Beauty Dunhuang Dance "Flying to Heaven" Action Breakdown Poster Generate a high-quality 16-grid action breakdown poster for "{argument name="theme" default="Dunhuang Dance 'Flying to Heaven'"}". Core requirements: - Layout: 4x4 square grid with 16 cells, each corresponding to a continuous dance step numbered 1-16, following the original choreography logic of the Dunhuang dance. - Aesthetic: Not a generic instructional chart, but a cinematic storyboard poster with a Dunhuang mural atmosphere, elegant classical style, and visual impact. - Character: Features a {argument name="character_image" default="classical and elegant beauty"} (gentle features, slender build, graceful aura), remaining consistent across all cells without character swaps. - Style: Dunhuang dance style, capturing the spirit of "Flying Apsaras" with flowing ribbons, graceful postures, and the original choreography's core movements like lifts, waist twists, and sleeve throws. - Costume: {argument name="costume_style" default="Classical Dunhuang Flying Apsaras attire"} in nude and camel tones with gold and stone-blue patterns (clouds, floral scrolls), using lightweight airy fabrics (gauze/chiffon) with traditional headpieces and ribbons. - Background: Mural-style background in earthy yellows and ochres, incorporating textures, auspicious clouds, and silhouettes with soft warm lighting to highlight the dancer. - Content per cell: Large step number, Chinese action title (dance terminology), concise Chinese key points, and trajectory arrows for ribbon and body movement. - Overall effect: Refined, consistent, and sophisticated, combining a tutorial feel with classical poster art and historical mural aesthetics.
Content: Structure: 16-grid layout + Dunhuang dance style + consistent character + costume/background details + per-cell content (number, title, key points, trajectory). A composite action breakdown poster prompt.
Pros: Clear structure, rich details, easy for AI to understand multi-level requirements.
Cons: Too many constraints may reduce flexibility and success rate; high demand for character consistency across cells.
Reference image: No obvious reference-image dependency
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