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

2026-02-06

AI Summary

This work addresses the challenge of preserving nonlinear creative histories and cognitive intent in product concept sketching, which traditional tools often fail to capture, leading to loss of design context. The authors propose DIMES, a novel system that integrates version control with generative AI for sketch-based design. DIMES introduces sGIT, a custom visual version control system that maps Git primitives to design operations, enabling implicit branching and multimodal commits combining strokes and voice annotations. It further incorporates the AEGIS stroke classification module and a neural transparency-based similarity metric to intelligently track and document creative actions. User studies demonstrate that expert designers achieve a 160% increase in exploration breadth, while novices attain a reproduction fidelity of 0.97 (baseline: 0.73) using AI-generated summaries. Additionally, AI-rendered visuals significantly enhance user purchase intent (4.2 vs. 3.1 on a 5-point scale).

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Economics, Online Markets and Human Computation: Economic aspects and information design of Web, GenAI, and cloud computing
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• design evolution
• cognitive intent
• sketching tools
• version control
• conceptual design

Innovation

• SketchGit
• Generative AI
• Visual Version Control
• Design Evolution Tracking
• Multi-modal Commit