  The visual anonymisation of sign language data is an essential task to
address privacy concerns raised by large-scale dataset collection. Previous
anonymisation techniques have either significantly affected sign comprehension
or required manual, labour-intensive work.
  In this paper, we formally introduce the task of Sign Language Video
Anonymisation (SLVA) as an automatic method to anonymise the visual appearance
of a sign language video whilst retaining the meaning of the original sign
language sequence. To tackle SLVA, we propose AnonySign, a novel automatic
approach for visual anonymisation of sign language data. We first extract pose
information from the source video to remove the original signer appearance. We
next generate a photo-realistic sign language video of a novel appearance from
the pose sequence, using image-to-image translation methods in a conditional
variational autoencoder framework. An approximate posterior style distribution
is learnt, which can be sampled from to synthesise novel human appearances. In
addition, we propose a novel \textit{style loss} that ensures style consistency
in the anonymised sign language videos.
  We evaluate AnonySign for the SLVA task with extensive quantitative and
qualitative experiments highlighting both realism and anonymity of our novel
human appearance synthesis. In addition, we formalise an anonymity perceptual
study as an evaluation criteria for the SLVA task and showcase that video
anonymisation using AnonySign retains the original sign language content.
