RealtimeGen: An Intervenable AI Image Generation System for Commercial Digital Art Asset Creators
Zejian Li, Ying Zhang, Shengzhe Zhou, Qi Liu, Jiesi Zhang, Haoran Xu, Shuyao Chen, Xiaoyu Chen, Lingyun Sun
Abstract
Recent advances in artificial intelligence-generated content (AIGC) have led to the rapid generation of high-quality images. AIGC has attracted the attention of commercial digital art asset creators. Traditional artist-led processes contrast with current AI tools that often reduce creators to passive roles. This study examines the integration of AI image generation into commercial digital art, emphasizing the importance of preserving creators’ creative autonomy. Our formative study (S1) involved interviews with commercial digital art creators, highlighting a need for greater control and transparency in AI-assisted painting. In response, we developed RealtimeGen, an integrated tool that merges human creativity with AI’s capabilities, allowing creators to intervene in the generative process. A user study (S2) comparing RealtimeGen with the popular AIGC tool Stable Diffusion was also carried out. The results showed its enhanced user experience and workflow compatibility. Our work contributes to understanding and improving AI-assisted painting workflows for commercial creators, offering them greater creative agency.