Story Continuation
4 papers with code • 3 benchmarks • 1 datasets
The task involves providing an initial scene that can be obtained in real world use cases. By including this scene, a model can then copy and adapt elements from it as it generates subsequent images.
Source: StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation
Benchmarks
These leaderboards are used to track progress in Story Continuation
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VIST
Most implemented papers
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation
Hence, we first propose the task of story continuation, where the generated visual story is conditioned on a source image, allowing for better generalization to narratives with new characters.
Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
xichenpan/ARLDM
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Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity.
Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction
Next, we conducted a preliminary user study using a story continuation task where AMT workers were given access to machine-generated story plots and asked to write a follow-up story.
How Far Can We Extract Diverse Perspectives from Large Language Models?
In this study, we investigate LLMs' capacity for generating diverse perspectives and rationales on subjective topics, such as social norms and argumentative texts.
