Browse State-of-the-Art › Story Continuation
Story Continuation
6 papers with code · 3 benchmarks · 1 dataset archive 2025-07-28
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
Description from the archive archive 2025-07-28; Papers-with-Code links inside it are rewritten to this site.
Benchmarks archive 2025-07-28
3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| FlintstonesSV (6 rows) | ContextualStory | ContextualStory: Consistent Visual Storytelling with... | code | — | Compare |
| PororoSV (6 rows) | ContextualStory | ContextualStory: Consistent Visual Storytelling with... | code | — | Compare |
| VIST (2 rows) | AR-LDM (SIS captions) | Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
6 shown of 6 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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13 Jul 2024 1 repository listedVisual storytelling involves generating a sequence of coherent frames from a textual storyline while maintaining consistency in characters and scenes.
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16 Nov 2023 1 repository listedTo see how far we can extract diverse perspectives from LLMs, or called diversity coverage, we employ a step-by-step recall prompting to generate more outputs from the model iteratively.
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22 Aug 2023 1 repository listed Syntology ran 14 of 16 samples · 2 unverifiedTo fill this gap, we collect comprehensive human annotations on three existing datasets, and introduce StoryBench: a new, challenging multi-task benchmark to reliably evaluate forthcoming text-to-video models.
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17 Feb 2023 1 repository listedNext, 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.
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20 Nov 2022 1 repository listedConditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity.
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13 Sep 2022 1 repository listed Syntology ran 2 of 3 samples · 1 unverifiedHence, 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.
Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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