Real-Time Intuitive AI Drawing System for Collaboration: Enhancing Human Creativity through Formal and Contextual Intent Integration

Jookyung Song, Mookyoung Kang, Nojun Kwak

Track: Paper

Keywords: real-time generative drawing, human–AI co-creation, multi-user collaboration, diffusion models, vision–language models, interactive installations

TL;DR: This paper presents a real-time AI system that interprets and integrates both formal and contextual intent from user sketches to create collaborative artworks.

Abstract: This paper presents a real-time generative drawing system that interprets and integrates both formal intent—the structural, compositional, and stylistic attributes of a sketch—and contextual intent—the semantic and thematic meaning inferred from its visual content - into a unified transformation process. Unlike conventional text-prompt-based generative systems, which primarily capture high-level contextual descriptions, our approach simultaneously analyzes ground-level intuitive geometric features such as line trajectories, proportions, and spatial arrangement, and high-level semantic cues extracted via vision–language models. These dual intent signals are jointly conditioned in a multi-stage generation pipeline that combines contour-preserving structural control with style and content-aware image synthesis. Implemented with a touchscreen-based interface and distributed inference architecture, the system achieves low-latency, two-stage transformation while supporting multi-user collaboration on shared canvases. The resulting platform enables participants, regardless of artistic expertise, to engage in synchronous, co-authored visual creation, redefining human–AI interaction as a process of co-creation and mutual enhancement.

Submission Number: 143