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

• URL: http://arxiv.org/abs/2312.07066v1
• Date: Tue, 12 Dec 2023 08:40:38 GMT
• Title: DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models
• Authors: Shengguang Wu, Mei Yuan, Qi Su
• Abstract summary: Visual storytelling is increasingly desired beyond real-world imagery. Current techniques, which typically use autoregressive decoders, suffer from low inference speed and are not well-suited for synthetic scenes. We propose a novel diffusion-based system DiffuVST, which models a series of visual descriptions as a single conditional denoising process.
• Score: 6.668241588219693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 image-to-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.

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