DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models

Abstract

Recent advances in image and video creation, especially AI-based image synthesis, have led to the production of numerous visual scenes that exhibit a high level of abstractness and diversity. Consequently, Visual Storytelling (VST), a task that involves generating meaningful and coherent narratives from a collection of images, has become even more challenging and is increasingly desired beyond real-world imagery. While existing VST techniques, which typically use autoregressive decoders, have made significant progress, they suffer from low inference speed and are not well-suited for synthetic scenes. To this end, we propose a novel diffusion-based system DIFFUVST, which models the generation of a series of visual descriptions as a single conditional denoising process. The stochastic and non-autoregressive nature of DIFFUVST at inference time allows it to generate highly diverse narratives more efficiently. In addition, DIFFUVST features a unique design with bi-directional text history guidance and multimodal adapter modules, which effectively improve inter-sentence coherence and imageto-text fidelity. Extensive experiments on the story generation task covering four fictional visual-story datasets demonstrate the superiority of DIFFUVST over traditional autoregressive models in terms of both text quality and inference speed.