pixel2motion
AI logo animation skill: turn raster logos into smooth SVG animation, animated HTML demos, GIF/video previews, and motion QA evidence.
- 评测生成时间(北京时间)
- 本报告引擎
- v3.10.0
- 当前引擎
- v3.16.0
本报告与当前引擎使用不同规则;原分数不会自动更新,不同版本的分数不宜直接对比。
进入后确认来源与额度,提交才会创建任务。
综合采用结论
核心能力可用,建议在受控范围内试用
- 基础评测完成+25/25确定性评分与静态安全扫描已完成
- README 有效证据+17/256,768 个去重后的有效字符
- 独立证据来源+4/201 类非重复证据,重复文件不叠加
- 仓库元数据+10/10已取得仓库状态与采用数据
- 活跃记录+5/5已取得最近提交时间
- AI 复核+15/15已完成结构化 AI 证据复核
Pixel2Motion工作流
README描述了从输入到输出的连续处理步骤,包括拟合、QA、动画生成和帧捕获。
左右滑动查看完整图示
- • Workflow 步骤3-7按顺序列出
- • 步骤4审计复杂曲线,步骤5构建HTML,步骤6捕获帧
- Workflow 步骤3-7提供了具体命令和参数,如render_overlay.py、animate_svg_showcase.py
- Deliverables 部分列出了logo.svg、motion.css、logo_motion.html等输出
- Requirements 部分指定Python 3.10+、Pillow、numpy、Chrome和Playwright
- Fitting Evidence 部分描述了QA检查点和对IoU的优化
- Publishing Checklist 提到需要添加LICENSE文件,但当前缺失
- 问题与用途描述
- 有效 README
- 可执行示例
- 输入、参数或工具说明
- 未发现已知高风险模式
- 缺少安装或接入步骤
- 缺少限制、权限或边界
- 缺少错误处理或排障
- 缺少错误处理和排障指南,用户遇到问题难以自行解决
- 未明确说明输入图像的具体要求(如尺寸、格式限制)
需要将静态logo转换为动画的设计师、希望生成可交互HTML动画演示的开发者、需要可审查的矢量拟合和运动QA证据的品牌项目、使用Codex或Claude进行自动化logo动画的用户
没有Python或浏览器环境的用户、需要处理复杂插画或照片级图像的用户(技能专注于logo)、寻求即用型动画模板而非定制工作流的用户
也有自己的公开项目?先看完证据,再用当前规则生成独立报告。
评测我的项目 →静态扫描不是安全保证,生产接入前仍应人工复核权限和数据边界。
- 01补充安装或接入步骤
- 02补充限制、权限或边界
- 03补充错误处理或排障
方法、证据与局限展开收起
GitHub Repository API
1 个文件 · 9,887 字符
v3.10.0 · AI 复核已启用(deepseek-chat)
- 静态评测不会安装或执行项目代码
- 安全扫描基于高信号文件与已知模式,不能替代人工审计
- 流行度只反映采用程度,不代表安全或工程质量
30 天热度趋势
README
www.pixel2motion.com
Better commercial Pixel2Motion services are coming online: polished logo-to-motion workflows, project-ready previews, and production support beyond the open-source skill.
中文:更完整的 Pixel2Motion 商业服务正在上线,面向更稳定的 logo-to-motion 交付、预览和项目支持。
Pixel2Motion - AI Logo Animation Skill
Raster logo → smooth minimal SVG → SVG logo animation → 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.
中文:Pixel2Motion 是一个把像素 logo 转成平滑 SVG,再生成品牌 motion、logo reveal、HTML 动效展示和视频预览的 Codex skill。它适合需要可审查矢量拟合、可复用 SVG 结构和可导出动图/透明视频的设计与开发场景。
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 0.65× custom timeline.
|
Horizon Pixel source Motion output |
Continuum Pixel source Motion output |
|
CueRecord Pixel source Motion output |
N Pixel source Motion output |
|
Focus Pixel source Motion output |
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 — 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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