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This paper explores avatar identification in creative story- telling applications where users create their own story and environment. We present a study that investigated the effects of avatar facial similarity to the user on the quality of the story product they create. The children told a story using a digital puppet-based storytelling system by inter- acting with a physical puppet box that was augmented with a real-time video feed of the puppet enactment. We used a facial morphing technique to manipulate avatar facial similarity to the user. The resulting morphed image was applied to each participants puppet character, thus creating a custom avatar for each child to use in story creation. We hypothesized that the more familiar avatars appeared to participants, the stronger the sense of character identification would be, resulting in higher story quality. The proposed rationale is that visual familiarity may lead participants to draw richer story details from their past real-life experiences. Qualitative analysis of the stories supported our hypothesis. Our results contribute to avatar design in children's creative storytelling applications.
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Hierarchically-Attentive RNN for Album Summarization and Storytelling
We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.
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- Award ID(s):
- 1633295
- PAR ID:
- 10066885
- Date Published:
- Journal Name:
- Empirical Methods in Natural Language Processing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/D17-1101
