pixel2motion
AI logo animation skill: turn raster logos into smooth SVG animation, animated HTML demos, GIF/video previews, and motion QA evidence.
Quick Overview
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Better commercial Pixel2Motion services are coming online: polished logo-to-motion workflows, project-ready previews, and production support beyond the open-source skill.
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Pixel2Motion - AI Logo Animation Skill
Raster logo â smooth minimal SVG â SVG logo animation â interactive HTML motion demo.
Commercial preview · Live interactive demo · Skill instructions · Companion skill: Pixel2SVG-HTML
Pixel2Motion is an open-source Codex and Claude skill for logo animation, SVG animation, and AI-assisted brand motion. It turns PNG, JPG, WebP, or screenshot logos into clean motion-ready SVG, then exports animated logo HTML, GIF/video previews, and motion QA evidence. Use it for animated logos, SVG logo reveals, logo motion design, pixel-to-vector reconstruction, and developer-friendly HTML animation workflows.
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Recommended review order: the motion gallery below, the commercial preview, the interactive demo, the fitting evidence, and then the implementation workflow.
Pixel-to-Motion Gallery
Each pairing shows the raster source next to the motion output, rendered from docs/index.html at the animation's default speed: Horizon 1900 ms, Continuum 2000 ms, Focus 1700 ms, N 2400 ms, and CueRecord at the page-default 0.65Ã custom timeline.
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Horizon Pixel source Motion output
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Continuum Pixel source Motion output
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CueRecord Pixel source Motion output
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N Pixel source Motion output
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Focus Pixel source Motion output
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Commercial Preview
The full interactive showcase lives in docs/index.html and is published through GitHub Pages at nolangz.github.io/pixel2motion. A more polished commercial Pixel2Motion service is coming online at www.pixel2motion.com, with project-ready previews and production support beyond the open-source skill.
Fitting Evidence
Every animation is authored against a QA-verified static vector. The CueRecord fitting sequence, read left to right:

The teal overlays are QA checkpoints, not the deliverable: the vector candidate is repeatedly compared against the raster source until mark scale, dot placement, wordmark baseline, and ink weight hold up â and only then is motion authored on top. The resulting clean semantic SVG, with mark, dot, and wordmark as separate addressable parts, becomes the final-frame contract for the animation.
Pixel2Motion optimizes IoU as a diagnostic, but smoothness and structure are the hard gates. A high-IoU jagged trace is rejected when a lower-complexity smooth vector explains the logo better. The static fitting methodology is documented in full in the companion Pixel2SVG-HTML project.
Deliverables
logo.svg: final static vector, structured for motionmotion.css: authored choreography targeting semantic SVG idslogo_motion.html: dependency-free showcase HTML with replay, slow motion, speed control, QA hooks, and atomic motion studiesmotion_spec.md: motion brief, principles applied, timeline, easing tokens, and QA notesoutputs/fit_iterations/*.png: geometry overlay evidenceoutputs/motion_frames/*.pngandoutputs/motion_strip.png: deterministic motion QA evidenceoutputs/final_render.pngandoutputs/html_render.png: static render checks
Workflow
- Read
SKILL.mdand the relevant reference files before fitting or choreographing. - Write the motion brief in
motion_spec.md: personality, usage context, part inventory, and choreography sketch. - Fit and QA the static vector:
python3 scripts/render_overlay.py logo.svg source.png \
--out outputs/fit_iterations/01_overlay.png \
--render-out outputs/final_render.png \
--report outputs/fit_metrics.json
- Audit complex curves when smoothness is a concern:
python3 scripts/svg_path_audit.py logo.svg \
--out-svg outputs/bezier_segments.svg \
--report outputs/bezier_audit.json
- Build the showcase HTML from the verified SVG and authored CSS:
python3 scripts/animate_svg_showcase.py logo.svg \
--css motion.css \
--out logo_motion.html \
--title "Logo Motion" \
--duration-hint 1500
- Capture deterministic motion frames:
python3 scripts/capture_motion_frames.py logo_motion.html \
--times 0,300,700,1000,1250,1500 \
--out outputs/motion_frames \
--strip outputs/motion_strip.png \
--compare-final outputs/final_render.png
- Probe risky motion windows when the animation uses draw-on, crossings, masks, or handoffs:
python3 scripts/probe_motion_continuity.py logo_motion.html \
--times 500,700,900 \
--probe "#draw-stroke:stroke-dashoffset,#pen-glint:offset-distance"
Requirements
- Python 3.10+
Pillowandnumpyfor image analysis helpers- Chrome or Chromium for geometry and HTML rendering
- Playwright for deterministic frame capture and motion continuity QA
Recommended local setup:
python3 -m venv .venv
.venv/bin/pip install pillow numpy playwright
.venv/bin/python -m playwright install chromium
If Chrome is not on the default path, set CHROME_BIN before running render checks:
export CHROME_BIN="/Applications/Google Chrome.app/Contents/MacOS/Google Chrome"
Repository Layout
SKILL.md: Codex-facing pixel-to-vector-to-motion workflowagents/openai.yaml: UI metadata for the skillreferences/: animation principles, motion personality, reveal patterns, HTML delivery template, and fitting referencesscripts/: helpers for tracing, rendering, overlays, path audits, showcase HTML generation, deterministic frame capture, and motion continuity probingdocs/: GitHub Pages demo, README preview images, GIFs, and fitting-process evidence
Publishing Checklist
- Confirm
SKILL.md,agents/openai.yaml,references/,scripts/, anddocs/are committed. - Keep generated logo deliverables, motion captures, local virtual environments, caches, and per-logo
outputs/out of git. - Enable GitHub Pages from branch
main, folder/docs, after the first push. - Add a
LICENSEfile before publishing if this repository should grant reuse rights. - After creating the GitHub repository, add the remote and push:
git remote add origin git@github.com:<owner>/pixel2motion.git
git branch -M main
git push -u origin main
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