  One of the factors that have hindered progress in the areas of sign language
recognition, translation, and production is the absence of large annotated
datasets. Towards this end, we introduce How2Sign, a multimodal and multiview
continuous American Sign Language (ASL) dataset, consisting of a parallel
corpus of more than 80 hours of sign language videos and a set of corresponding
modalities including speech, English transcripts, and depth. A three-hour
subset was further recorded in the Panoptic studio enabling detailed 3D pose
estimation. To evaluate the potential of How2Sign for real-world impact, we
conduct a study with ASL signers and show that synthesized videos using our
dataset can indeed be understood. The study further gives insights on
challenges that computer vision should address in order to make progress in
this field.
  Dataset website: http://how2sign.github.io/
