DramaFlow: A Visual DAG-Orchestrated Framework for AI-Driven Multimodal Content Generation
Year: 2026
Type: conference-paper
Abstract: The rapid advancement of AI-generated content (AIGC) has enabled automated production of multimodal data, including text, images, audio, and video. However, existing tools remain fragmented across platforms, making it difficult to construct scalable and reusable generation pipelines. To address this limitation, we propose DramaFlow, a visual workflow orchestration framework for multimodal content generation. The framework introduces a unified node abstraction to encapsulate heterogeneous AI services, and models workflows as directed acyclic graphs (DAGs) for automatic dependency-aware scheduling. Furthermore, we design a template-driven parallel dispatcher to support scalable batch generation with multi-source alignment and dependency remapping. Experimental results demonstrate that DramaFlow reduces manual operations by approximately 67% compared to manual baselines while maintaining flexible workflow construction. The proposed framework provides an effective solution for end-to-end multimodal content generation.
Institutions: Shenzhen Polytechnic University, Shenzhen University
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7204253175