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Progressive Transformers for End-to-End Sign Language Production

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12356))

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Abstract

The goal of automatic Sign Language Production (SLP) is to translate spoken language to a continuous stream of sign language video at a level comparable to a human translator. If this was achievable, then it would revolutionise Deaf hearing communications. Previous work on predominantly isolated SLP has shown the need for architectures that are better suited to the continuous domain of full sign sequences.

In this paper, we propose Progressive Transformers, the first SLP model to translate from discrete spoken language sentences to continuous 3D sign pose sequences in an end-to-end manner. A novel counter decoding technique is introduced, that enables continuous sequence generation at training and inference. We present two model configurations, an end-to-end network that produces sign direct from text and a stacked network that utilises a gloss intermediary. We also provide several data augmentation processes to overcome the problem of drift and drastically improve the performance of SLP models.

We propose a back translation evaluation mechanism for SLP, presenting benchmark quantitative results on the challenging RWTH-PHOENIXWeather-2014T (PHOENIX14T) dataset and setting baselines for future research. Code available at https://github.com/BenSaunders27/ProgressiveTransformersSLP.

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Notes

  1. 1.

    Glosses are a written representation of sign, defined as minimal lexical items.

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Acknowledgements

This work received funding from the SNSF Sinergia project ‘SMILE’ (CRSII2 160811), the European Union’s Horizon2020 research and innovation programme under grant agreement no. 762021 ‘Content4All’ and the EPSRC project ‘ExTOL’ (EP/R03298X/1). This work reflects only the authors view and the Commission is not responsible for any use that may be made of the information it contains. We would also like to thank NVIDIA Corporation for their GPU grant.

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Authors and Affiliations

  1. University of Surrey, Guildford, England

    Ben Saunders, Necati Cihan Camgoz & Richard Bowden

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  1. Ben Saunders
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  2. Necati Cihan Camgoz
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Correspondence to Ben Saunders .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Saunders, B., Camgoz, N.C., Bowden, R. (2020). Progressive Transformers for End-to-End Sign Language Production. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12356. Springer, Cham. https://doi.org/10.1007/978-3-030-58621-8_40

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