Advanced Animation in Mathematica

Dickson, Stewart

doi:10.3888/tmj.15-2

Cited by 4 publications

(3 citation statements)

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“…When learning to recognize fingerspelling, it is important to have the whole visual field of the person or avatar [ 5 ]. In fact most still-frame tools have the same issue as they only show pictures or drawings of isolated hands Motion that did not prevent self-intersection of the fingers, such as seen in [ 3 , 12 ]. Human fingers will naturally move to avoid each other in transitions between letters, and any tool that does not prevent collisions in the fingers will not provide the realistic fingerspelling motion needed by students.…”

Section: Limitations Of Current Practice Toolsmentioning

confidence: 99%

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Enabling Real-Time 3D Display of Lifelike Fingerspelling in a Web App

Montgomery

McDonald

Gong

et al. 2020

Lecture Notes in Computer Science

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Fingerspelling receptive skills remain among the most difficult aspects of sign language for hearing people to learn due to the lack of access to practice tools that reproduce the natural motion of human signing. This problem has been exacerbated in recent years by the move from desktop to mobile technologies which has rendered prior software platforms less accessible to general users. This paper explores a web-enabled 3D rendering architecture that enables real-time fingerspelling on a human avatar that can address these issues. In addition it is capable of producing more realistic motion than prior efforts that were video-based and provides greater interactivity and customization that will support further enhancements to self-practice tools for fingerspelling reception.

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Section: Limitations Of Current Practice Toolsmentioning

confidence: 99%

“…Motion that did not prevent self-intersection of the fingers, such as seen in [ 3 , 12 ]. Human fingers will naturally move to avoid each other in transitions between letters, and any tool that does not prevent collisions in the fingers will not provide the realistic fingerspelling motion needed by students.…”

Section: Limitations Of Current Practice Toolsmentioning

confidence: 99%

Enabling Real-Time 3D Display of Lifelike Fingerspelling in a Web App

Montgomery

McDonald

Gong

et al. 2020

Lecture Notes in Computer Science

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“…The contrast between human collision avoidance and naive interpolation in an avatar [30] Accurately portraying this high level of realism in an avatar entails large computational requirements. For this reason, some previous efforts sacrificed realism to gain real-time speeds by using a simplified 3D avatar that did not accurately portray a human hand and/or did not prevent collisions [31] [32]. Another early approach to collision avoidance was to move the hand into a neutral position between each letter [33].…”

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confidence: 99%

Using N-Gram Analytics to Improve Automatic Fingerspelling Generation

McDonald¹,

Wolfe²,

Baowidan³

et al. 2017

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Fingerspelling recognition is one of the last skills acquired, due to the complex nature of fingerspelling and a lack of display technology that is sufficiently natural for recognition practice. This paper describes a corpus-based study utilizing an n-gram extension to ELAN to gain a deeper understanding of deletion and coarticulation in fingerspelling. The analysis shows that coarticulation and deletion increase with fingerspelling speed and that deletions form an increasing percentage of the modifications at shorter durations. Insights from the study informed strategies to improve current avatar-based fingerspelling generation.

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