SOTAVerified

Story Continuation

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 10 papers

TitleStatusHype
ContextualStory: Consistent Visual Storytelling with Spatially-Enhanced and Storyline ContextCode0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
StoryBench: A Multifaceted Benchmark for Continuous Story VisualizationCode1
Conveying the Predicted Future to Users: A Case Study of Story Plot PredictionCode0
Synthesizing Coherent Story with Auto-Regressive Latent Diffusion ModelsCode2
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story ContinuationCode0
Telling Stories through Multi-User Dialogue by Modeling Character Relations—0
Forms of Anaphoric Reference to Organisational Named Entities: Hoping to widen appeal, they diversified—0
Event versus entity co-reference: Effects of context and form of referring expression—0
Quality Signals in Generated Stories—0
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All datasetsFlintstonesSVPororoSVVIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AR-LDM (DII captions)FID17.03—Unverified
2AR-LDM (SIS captions)FID16.95—Unverified