RealtimeGen: An Intervenable AI Image Generation System for Commercial Digital Art Asset Creators

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.

Keywords:

• Commercial digital art asset creators
• creativity support
• generative AI
• human-AI interaction
• painting support tool
• user experience design

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

This paper is supported by Provincial Key Research and Development Plan of Zhejiang Province [No. 2024C01250(SD2)], National Natural Science Foundation of China [Grant No. 62006208 and 62207024], and Youth Program of Humanities and Social Sciences of the Ministry of Education [No. 23YJCZH338].

Notes on contributors

Zejian Li

Zejian Li is an assistant researcher at School of Software Technology, Zhejiang University. He obtained the Ph.D. degree from Zhejiang University in 2019. His research interests include image generation models and Human-AI cocreation.

Ying Zhang

Ying Zhang is a PhD student majoring in electronic information, from the School of Software, Zhejiang University. Her main research interests are in human-computer interaction and image generation.

Shengzhe Zhou

Shengzhe Zhou is a master’s student majoring in electronic information from the School of Software, Zhejiang University. His main research interest is image generation.

Qi Liu

Qi Liu is a PhD student majoring in electronic information, from the School of Software, Zhejiang University. Her main research interests are in human-computer interaction and image generation.

Jiesi Zhang

Jiesi Zhang is an industrial design engineering master’s student from the School of Software, Zhejiang University.

Haoran Xu

Haoran Xu is a master’s student majoring in computer science from the School of Computer Science and Technology of Zhejiang University.

Shuyao Chen

Shuyao Chen is an industrial design undergraduate from Zhejiang University.

Xiaoyu Chen

Xiaoyu Chen received Ph.D. degrees in Design Study from the College of Computer Science, Zhejiang University, Hangzhou, China, in 2022. His research interests include emotional design, product design and human-computer interaction.

Lingyun Sun

Lingyun Sun is a professor at the College of Computer Science and Technology, Zhejiang University. He is the deputy director of International Design Institute of Zhejiang University. His research interests include human-computer interaction, creative intelligence, and information and interaction design.