2dimg2motion.skill

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2dimg2motion.skill -- not a filter that "makes an image move", but a runnable framework for generating game animation frames

Spritesheet delivery pipeline via key-pose redrawing, identity locking, and limb topology constraints,
it turns every instinct of "how should this character move?" into an executable, verifiable, reusable animation-generation pipeline.
Input one 2D character, creature, weapon, vehicle, or prop image,
and output a style-consistent transparent action sequence ready to import into a game engine.

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What Is This

2dimg2motion.skill is a 2D game-animation generation skill for Codex / Claude Code / Agent Skills. It takes one static character, creature, vehicle, weapon, or prop image and generates a style-consistent transparent action sequence that can be packed into a spritesheet.

It is not a "single-image motion filter". Filters usually translate, rotate, scale, or warp local image layers; the core of this skill is whole-character key-pose redraw: first lock the character identity, then let the model redraw complete key poses, then generate in-between frames, with local scripts handling transparency, canvas normalization, packing, and validation.

Why It Exists

AI can easily draw characters that look like they belong to the same series, but it is much harder to draw continuous motion for the same character. Game animation adds more requirements:

• every frame must be a transparent RGBA PNG;
• character proportions, foot baseline, and canvas size must stay stable;
• weapons, arms, horns, tails, and wings must not swap sides or disappear;
• keyframes and in-betweens must form readable motion;
• the final output must be directly usable in Godot, Unity, Cocos, or a custom 2D engine.

This repository turns those lessons into an executable workflow: analyze the baseline image, establish the identity lock, design action beats, generate a shared key-pose sheet, generate in-betweens, remove the background, normalize frames, pack assets, validate structure, and visually inspect the result.

Quick Start

1. Install The Skill

Put the repository into your Agent Skills directory. Keep the repository structure intact, because the main skill, sub-skills, scripts, references, and project knowledge files refer to each other.

Codex example:

git clone https://github.com/WU-HAOTIAN34/2dimg2motion.git $env:USERPROFILE\.codex\skills\2dimg2motion

Claude Code example:

git clone https://github.com/WU-HAOTIAN34/2dimg2motion.git ~/.claude/skills/2dimg2motion

You can also run it directly from this repository during local development. Confirm the dependency:

python -m pip install pillow

2. Use The Main Skill To Generate Animation

In Codex or Claude Code, provide a baseline image and describe the motion:

/2dimg2motion use sample/s3.png to generate an arm-swing attack animation

Or:

Generate a walk action from this character image.
/2dimg2motion use sample/s7.png to generate a sword-slash action.
/2dimg2motion use sample/s1.png to make a heavy ground-smash attack animation.

The main skill runs the full workflow: analyze the baseline image, establish the identity lock, design key poses at 02/05/08/11, generate in-betweens, remove the background, normalize frames, pack the spritesheet, and output preview.gif and manifest.json.

3. Use The Baseline Standardization Sub-Skill

If the input image is too large, tightly cropped, visibly white-backed, or lacks enough transparent margin for attacks or walking motion, call the standardization sub-skill first:

/img2mo-std s7
/img2mo-std sample\s7.png

It resolves the image path and calls the script to create a standardized baseline image:

python scripts\standardize_baseline.py sample\s7.png

Useful options:

python scripts\standardize_baseline.py sample\s7.png --check-only
python scripts\standardize_baseline.py sample\s7.png --subject-max 360 --margin-ratio 0.75
python scripts\standardize_baseline.py sample\s7.png --output sample\s7-standard.png

Default output:

sample\<input-stem>-standard.png

The standardized image should become the 00 and 13 baseline frame for later animation generation.

4. Use The Learning Sub-Skill

If you have reference videos, existing outputs, failed attempts, GIFs, spritesheets, Spine assets, or frame folders, the skill can extract reusable project knowledge from them:

/img2mo-learn output\s3-arm-swing-attack
/img2mo-learn motion\some-reference-folder
/img2mo-learn sample\attack-reference.mp4

Learned notes are written to:

img2mo-knowledge/
|-- index.md
|-- learnings.jsonl
|-- action-patterns.md
|-- style-patterns.md
|-- prompt-patterns.md
`-- failures.md

Future animation generations read this project-level knowledge first, improving action timing, prompt clauses, canvas margins, and failure checks.

5. Use Scripts Directly

Scripts are for non-creative post-processing and validation only. They do not draw motion.

Standardize a baseline image:

python scripts\standardize_baseline.py sample\s7.png

Convert transparent fullframe PNGs into a white-background GIF preview:

python scripts\fullframes_to_gif.py output\s3-arm-swing-attack\fullframe --output output\s3-arm-swing-attack\preview.gif --duration-ms 75

Validate the 14-frame delivery structure:

python scripts\validate_14frame_pattern.py --baseline sample\s3-standard.png --keyframes-dir output\s3-arm-swing-attack\keyframe --fullframes-dir output\s3-arm-swing-attack\fullframe --preview output\s3-arm-swing-attack\preview.gif --prefix s3-arm-swing-attack

On success, it prints:

OK

Showcase

Ronin - Slash	Golem - Smash	Monster - Claw	Giant - Sword Swing	Worker - Walk	Beetle - Crawl

Input And Output

The input is usually one static PNG:

sample/s3.png

Describe the action you want:

use sample/s3.png to generate an arm-swing attack animation

The output is a standard delivery bundle:

output/<action-id>/
|-- keyframe-prompts.md   # prompts and identity lock for 02/05/08/11
|-- keyframe/             # 4 keyframes at fixed indices 02, 05, 08, 11
|-- fullframe/            # 14 transparent RGBA sequence frames, fixed indices 00-13
|-- spritesheet.png       # horizontal packed spritesheet
|-- contact-sheet.png     # visual review sheet
|-- preview.gif           # 14-frame white-background playback preview
`-- manifest.json         # canvas, frame order, topology lock, keyframe indices, and processing notes

fullframe/*.png contains the transparent frames you should import into your engine. preview.gif is only a human-readable playback preview.

Core Mechanisms

1. Identity Lock

Before generation, record the character invariants: face, eyes, silhouette, proportions, palette, outline, clothing, weapon, horns, tail, wings, claws, markings, and other accessories. Every later prompt inherits this identity lock.

2. Topology Lock

For attacks, waves, sword swings, tail sweeps, and similar actions, the skill records the active limb and anchor limb in screen space, for example:

activeLimb: screen-right arm
anchorLimb: screen-left arm

This reduces failures like "the right hand suddenly becomes the left hand", "the weapon switches hands", or "the tail root disappears".

3. 14-Frame Structure

Default actions use fixed anchors:

00 -> 02 -> 05 -> 08 -> 11 -> 13

00 and 13 are the baseline image. 02/05/08/11 are the four model-generated key poses. The other frames are in-betweens generated from adjacent anchors. This structure keeps short actions compact and easy to validate.

4. Whole-Character Redraw

Keyframes and in-betweens must be generated by an image model as complete character poses. Scripts may only perform non-creative post-processing: baseline standardization, cell splitting, chroma-key removal, canvas normalization, GIF generation, spritesheet packing, and validation.

5. Verifiable Delivery

The repository includes a validator that checks frame count, naming, RGBA mode, canvas size, transparent corners, whether 00/13 match, whether keyframes are pixel-identical to their corresponding fullframes, and whether the GIF is a 14-frame white-background preview.

Supported Action Types

Action	Common Beats
idle	settle -> rise -> settle
walk / move	contact -> down -> passing -> up -> opposite contact
attack	guard -> anticipation -> acceleration -> contact -> follow-through -> recovery
block	raise guard -> hold -> return
hit / suffer	impact -> recoil -> squash/stretch -> recovery
death	imbalance -> fall -> impact -> rest
born / spawn	small/curled shape -> unfold -> full identity
skill / cast	anticipation -> charge -> peak cast -> recovery

Workflow

1. Standardize the baseline image: when needed, crop and resize the input image onto a transparent padded canvas to prevent action poses from going out of bounds.
2. Analyze the character: record body structure, colors, style, face, limbs, weapons, accessories, and uncertain areas.
3. Establish locks: write the identity lock, topology lock, weapon/prop ownership, and canvas constraints.
4. Design key poses: design action beats for 02/05/08/11.
5. Generate the key-pose sheet: generate the four key poses together to reduce identity drift from independent calls.
6. Generate in-betweens: generate 8 in-between frames with the fixed 1/2/2/2/1 insertion plan.
7. Post-process: remove the chroma-key background and normalize canvas, scale, center, and foot baseline.
8. Package deliverables: output fullframe, keyframe, spritesheet, contact-sheet, preview.gif, and manifest.
9. Validate and visually inspect: run structural validation, then inspect the contact sheet and GIF for hand swaps, clipping, cyan fringe, scale popping, or unreadable motion.

Repository Structure

.
|-- SKILL.md                  # main skill instructions and full generation contract
|-- references/               # key-pose redraw and motion prompt references
|-- skills/img2mo-std/        # baseline standardization sub-skill
|-- scripts/
|   |-- standardize_baseline.py
|   |-- fullframes_to_gif.py
|   `-- validate_14frame_pattern.py
|-- sample/                   # input sample images
|-- examples/                 # README showcase assets
|-- motion/                   # local motion reference library
`-- img2mo-knowledge/         # project-level learned knowledge

Design Principles

• Prefer whole-character generation: do not fake key poses by rotating local limbs with scripts.
• Generate shared key poses together: do not generate 02/05/08/11 as four independent calls.
• Keep 00/13 identical to the baseline: loop endpoints must be the same baseline frame.
• Use one scale strategy per batch: avoid frame-by-frame auto-fit that causes scale popping.
• Do not skip visual inspection: validation scripts catch structural issues, not every animation failure.
• Keep final directories clean: final output directories should contain deliverables only, not failed drafts or model source sheets.

Dependencies

• Python 3.x
• Pillow
• Codex / Claude Code / Agent Skills style runtime
• Available image-generation capability for key poses and in-between frames

License

MIT © 2026 Haotian Wu

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Use Case

Moving Tower Defense Battle WeChat Mini Game · Tower Defense Strategy