Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences

Xudong Hong, Asad Sayeed, Khushboo Mehra, Vera Demberg, Bernt Schiele

Abstract
Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent, diverse, and visually grounded compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and diverse than stories generated with the current state-of-the-art model. Our code, image features, annotations and collected stories are available at https://vwprompt.github.io/.
Anthology ID:
2023.tacl-1.33
Volume:
Transactions of the Association for Computational Linguistics, Volume 11
Month:
Year:
2023
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
565–581
Language:
URL:
https://aclanthology.org/2023.tacl-1.33
DOI:
10.1162/tacl_a_00553
Bibkey:
Cite (ACL):
Xudong Hong, Asad Sayeed, Khushboo Mehra, Vera Demberg, and Bernt Schiele. 2023. Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences. Transactions of the Association for Computational Linguistics, 11:565–581.
Cite (Informal):
Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences (Hong et al., TACL 2023)
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https://preview.aclanthology.org/emnlp22-frontmatter/2023.tacl-1.33.pdf
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