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Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses
December 20, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Lucas Ventura, Amanda Duarte, Xavier Giro-i-Nieto
arXiv ID
2012.10941
Category
cs.CV: Computer Vision
Cross-listed
cs.AI
Citations
39
Venue
arXiv.org
Last Checked
6 months ago
Abstract
Recent work have addressed the generation of human poses represented by 2D/3D coordinates of human joints for sign language. We use the state of the art in Deep Learning for motion transfer and evaluate them on How2Sign, an American Sign Language dataset, to generate videos of signers performing sign language given a 2D pose skeleton. We evaluate the generated videos quantitatively and qualitatively showing that the current models are not enough to generated adequate videos for Sign Language due to lack of detail in hands.
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