@conference {cDuarte20, title = {How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language}, booktitle = {CVPR 2021}, year = {2021}, month = {06/2021}, abstract = {

Sign Language is the primary means of communication for the majority of the Deaf community. One of the factors that has hindered the progress in the areas of automatic sign language recognition, generation, and translation is the absence of large annotated datasets, especially continuous sign language datasets, i.e. datasets that are annotated and segmented at the sentence or utterance level. Towards this end, in this work we introduce How2Sign, a work-in-progress dataset collection. How2Sign consists of a parallel corpus of 80 hours of sign language videos (collected with multi-view RGB and depth sensor data) with corresponding speech transcriptions and gloss annotations. In addition, a three-hour subset was further recorded in a geodesic dome setup using hundreds of cameras and sensors, which enables detailed 3D reconstruction and pose estimation and paves the way for vision systems to understand the 3D geometry of sign language.





}, author = {Amanda Duarte and S. Palaskar and Lucas Ventura and Ghadiyaram, Deepti and DeHaan, Kenneth and F. Metze and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cVentura20, title = {Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses}, booktitle = {ECCV 2020 Workshop on Sign Language recognition, Production and Translation (SLRTP)}, year = {2020}, month = {08/2020}, 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.

}, author = {Lucas Ventura and Amanda Duarte and Xavier Gir{\'o}-i-Nieto} }