Git for Sketches: An Intelligent Tracking System for Capturing Design Evolution

TL;DR

This paper introduces DIMES, an AI-enhanced web-based system with a custom visual version control for sketches, significantly improving concept exploration, knowledge transfer, and user acceptance in design evolution tracking.

Contribution

It presents a novel system combining visual version control, AI classification, and generative AI to better capture and analyze design evolution and cognitive intent.

Findings

160% increase in concept exploration breadth

Higher fidelity in novice replication (0.97 vs. 0.73)

AI-generated summaries and renderings received higher user acceptance

Abstract

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.