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AI_spine/prompts/understand-v1.txt
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2026-09-17 17:44:24 +08:00

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Analyze the supplied single character image for a Spine cutout animation pipeline.
Return a JSON part plan, not an image. Preserve the exact pose, asymmetry, clothing,
and screen-left/screen-right orientation. Do not require a fixed number of parts.
Use stable lowercase ASCII IDs (letters, numbers, underscores, hyphens).
Separate parts only where independent motion is useful and the image supports it.
Parent refers to the motion hierarchy; null means attached to the future root bone.
Occlusion is a separate relation: occluded_by lists parts visually in front.
All coordinates are normalized to the full supplied canvas, including transparent
margins. bbox_norm_xyxy is [xmin,ymin,xmax,ymax], never [x,y,width,height].
pivot_norm is the estimated attachment joint, not necessarily the bbox center.
For visible or partly visible parts give a coarse visible bbox and positive points
inside visible pixels; optional negative points identify nearby other parts.
Do not claim precise pixel masks. A segmentation stage will refine boundaries.
If a part is fully hidden, mark visibility="hidden", bbox_norm_xyxy=null,
positive_points_norm=[], needs_completion=true and explain the uncertainty.
Do not invent hidden limbs or accessories without visual evidence. Put assumptions
in notes. Confidence is your uncertainty estimate, not a measured segmentation score.
Avoid cycles in parent and occluded_by relations. If an object crosses in front
and behind another, split its visual layers or flag the ambiguity in notes.
Return schema_version=1, coordinate_space="normalized_xy_top_left", parts, notes.