Improved Visual Story Generation with Adaptive Context Modeling
Zhangyin Feng Yuchen Ren Xinmiao Yu Xiaocheng Feng Duyu Tang Shuming Shi Bing Qin
Diffusion models developed on top of powerful text-to-image generation models
like Stable Diffusion achieve remarkable success in visual story generation.
However, the best-performing approach considers historically generated results
as flattened memory cells, ignoring the fact that not all preceding images
contribute equally to the generation of the characters and scenes at the
current stage. To address this, we present a simple method that improves the
leading system with adaptive context modeling, which is not only incorporated
in the encoder but also adopted as additional guidance in the sampling stage to
boost the global consistency of the generated story. We evaluate our model on
PororoSV and FlintstonesSV datasets and show that our approach achieves
state-of-the-art FID scores on both story visualization and continuation
scenarios. We conduct detailed model analysis and show that our model excels at
generating semantically consistent images for stories.