Improved Visual Story Generation with Adaptive Context Modeling (2023.findings-acl)
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| Challenge: | a recent work shows that diffusion models generate images of high resolution and semantic consistency to text prompts. |
| Approach: | They propose a method that uses adaptive context modeling to improve leading system . they evaluate their method on pororoSV and FlintstonesSV datasets . |
| Outcome: | The proposed method achieves state-of-the-art FID scores on pororo and Flintstones datasets. |
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| Challenge: | Experimental results show that our model generates visual stories with consistent and coreferent character mentions compared to baselines and state-of-the-art systems. |
| Approach: | They propose a character-centric approach to visual story generation that uses visual and textual character coreference chains to enrich the VIST benchmark. |
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Improving Generation and Evaluation of Visual Stories via Semantic Consistency (2021.naacl-main)
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| Challenge: | Story visualization is an underexplored task that requires a generative model to generate images . prior work has focused on image generation but there is room for improvement . |
| Approach: | They propose to add a dual learning framework to reinforce semantic alignment between story and generated images and a copy-transform mechanism to model sequentially-consistent story visualization. |
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Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
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| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
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Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication (2020.coling-main)
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| Challenge: | Existing methods for visual storytelling construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. |
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Hierarchical Neural Story Generation (P18-1)
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| Challenge: | a hierarchical model that generates a premise and then conditions on it creates fluent text . a novel form of model fusion improves the relevance of the story to the prompt . |
| Approach: | They use a hierarchical model that first generates a premise, then transforms it into a text . they use fusion to improve relevance of the story to the prompt and add a gated mechanism to model context . |
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Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences (2023.tacl-1)
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| Challenge: | Existing work on image-based story generation lacks coherent plots for story generation. |
| Approach: | They propose to use image sequences to generate stories from a dataset that has more coherent plots. |
| Outcome: | The proposed model produces more coherent, visually grounded and diverse stories than existing models. |
Cue Me In: Content-Inducing Approaches to Interactive Story Generation (2020.aacl-main)
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| Challenge: | Existing methods for automatic story generation focus on one-shot generation, but we focus on interactive story generation. |
| Approach: | They propose two ways to incorporate user-provided cue phrases into automatic story generation. |
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Neural Text Generation in Stories Using Entity Representations as Context (N18-1)
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| Challenge: | Existing models of text generation that explicitly represent entities are based on the use of words and entities. |
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| Challenge: | Existing methods for visual storytelling suffer from low inference speed and are not well-suited for synthetic scenes. |
| Approach: | They propose a diffusion-based system that generates visual descriptions as a single conditional denoising process. |
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Stylized Story Generation with Style-Guided Planning (2021.findings-acl)
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| Challenge: | Current storytelling systems focus more on generating stories with coherent plots regardless of the narration style. |
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