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Complex animated transitions may be easier to understand when divided into separate, consecutive stages. However, effective staging requires careful attention to both animation semantics and timing parameters. We present Gemini^2, a system for creating staged animations from a sequence of chart keyframes. Given only a start state and an end state, Gemini^2 can automatically recommend intermediate keyframes for designers to consider. The Gemini^2 recommendation engine leverages Gemini, our prior work, and GraphScape to itemize the given complex change into semantic edit operations and to recombine operations into stages with a guided order for clearly conveying the semantics. To evaluate Gemini^2's recommendations, we conducted a human-subject study in which participants ranked recommended animations from both Gemini^2 and Gemini. We find that Gemini^2's animation recommendation ranking is well aligned with subjects' preferences, and Gemini^2 can recommend favorable animations that Gemini cannot support.
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Empirical Investigation of Users' Preferred Timing Parameters for American Sign Language Animations
To make it easier to add American Sign Language (ASL) to websites, which would increase information accessibility for many Deaf users, we investigate software to semi-automatically produce ASL animation from an easy-to-update script of the message, requiring us to automatically select the speed and timing for the animation. While we can model speed and timing of human signers from video recordings, prior work has suggested that users prefer animations to be slower than videos of humans signers. However, no prior study had systematically examined the multiple parameters of ASL timing, which include: sign duration, transition time, pausing frequency, pausing duration, and differential signing rate. In an experimental study, 16 native ASL signers provided subjective preference judgements during a side-by-side comparison of ASL animations in which each of these five parameters was varied. We empirically identified and report users' preferences for each of these individual timing parameters of ASL animation.
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- Award ID(s):
- 1462280
- PAR ID:
- 10170036
- Date Published:
- Journal Name:
- Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 7
- 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/10.1145/3334480.3382989
