用智能版本控制追踪设计草图演化，提升创意探索与知识传递效率。
Git for Sketches: An Intelligent Tracking System for Capturing Design Evolution
- 将Git机制映射到设计动作，支持笔迹+语音的多模态提交与隐式分支。
- 专家使用后概念探索广度提升160%，新手复现精度达0.97（基准0.73）。
- 适合设计教育、创意团队协作，尤其利于记录和传递设计意图。
产品概念设计中，捕捉非线性历史与认知意图至关重要。传统绘图工具常丢失上下文。我们提出DIMES（设计创意管理与演化捕捉系统），一个基于Web的环境，包含自研视觉版本控制系统sGIT和生成式AI模块。sGIT中的AEGIS模块利用混合深度学习与机器学习模型，可分类六类笔触。系统将Git基本操作映射为设计行为，实现隐式分支与多模态提交（笔迹数据+语音意图）。对比实验显示，使用DIMES的专家在概念探索广度上提升160%；生成式AI生成的叙事摘要显著提升知识传递效果：新手复现精度（神经透明度余弦相似度）达0.97，优于人工摘要的0.73。生成渲染也获更高用户接受度（购买意愿评分4.2 vs 3.1）。本研究证明，智能版本控制能连接创作行为与认知记录，为设计教育提供新范式。
原文摘要 · Abstract (English)
During product conceptualization, capturing the non-linear history and cognitive intent is crucial. Traditional sketching tools often lose this context. We introduce DIMES (Design Idea Management and Evolution capture System), a web-based environment featuring sGIT (SketchGit), a custom visual version control architecture, and Generative AI. sGIT includes AEGIS, a module using hybrid Deep Learning and Machine Learning models to classify six stroke types. The system maps Git primitives to design actions, enabling implicit branching and multi-modal commits (stroke data + voice intent). In a comparative study, experts using DIMES demonstrated a 160% increase in breadth of concept exploration. Generative AI modules generated narrative summaries that enhanced knowledge transfer; novices achieved higher replication fidelity (Neural Transparency-based Cosine Similarity: 0.97 vs. 0.73) compared to manual summaries. AI-generated renderings also received higher user acceptance (Purchase Likelihood: 4.2 vs 3.1). This work demonstrates that intelligent version control bridges creative action and cognitive documentation, offering a new paradigm for design education.
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