Openstory++: A Large-scale Dataset and Benchmark for Instance-aware
  Open-domain Visual Storytelling
Authors: name: Zilyu Ye, name: Jinxiu Liu, name: Ruotian Peng, name: Jinjin Cao, name: Zhiyang Chen, name: Yiyang Zhang, name: Ziwei Xuan, name: Mingyuan Zhou, name: Xiaoqian Shen, name: Mohamed Elhoseiny, name: Qi Liu, name: Guo-Jun Qi

Recent image generation models excel at creating high-quality images from
brief captions. However, they fail to maintain consistency of multiple
instances across images when encountering lengthy contexts. This inconsistency
is largely due to in existing training datasets the absence of granular
instance feature labeling in existing training datasets. To tackle these
issues, we introduce Openstory++, a large-scale dataset combining additional
instance-level annotations with both images and text. Furthermore, we develop a
training methodology that emphasizes entity-centric image-text generation,
ensuring that the models learn to effectively interweave visual and textual
information. Specifically, Openstory++ streamlines the process of keyframe
extraction from open-domain videos, employing vision-language models to
generate captions that are then polished by a large language model for
narrative continuity. It surpasses previous datasets by offering a more
expansive open-domain resource, which incorporates automated captioning,
high-resolution imagery tailored for instance count, and extensive frame
sequences for temporal consistency. Additionally, we present Cohere-Bench, a
pioneering benchmark framework for evaluating the image generation tasks when
long multimodal context is provided, including the ability to keep the
background, style, instances in the given context coherent. Compared to
existing benchmarks, our work fills critical gaps in multi-modal generation,
propelling the development of models that can adeptly generate and interpret
complex narratives in open-domain environments. Experiments conducted within
Cohere-Bench confirm the superiority of Openstory++ in nurturing high-quality
visual storytelling models, enhancing their ability to address open-domain
generation tasks. More details can be found at https://openstorypp.github.io/